python /home/admin/mtr/script_for_cron.py -j coverage -m 9 -a '' -s coverage -M 0 -S 0 -U 100,100,120 import MySQLdb succeeded root_folder /data_2/data_log/job/2025/April/23042025/coverage/ git_velours : /home/admin/workarea/git/Velours/ out_folder_name htmlcov output_folder /data_2/data_log/job/2025/April/23042025/coverage/htmlcov new path : /data_2/data_log/job/2025/April/23042025/coverage/ command : coverage3 run /home/admin/workarea/git/Velours/python/tests/python_tests.py --short_python3 `cat ~/.fotonower_pass/bdd.py.pass` cat: /home/admin/.fotonower_pass/bdd.py.pass: Aucun fichier ou dossier de ce type import MySQLdb succeeded Import error (python version) python version = 3 warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data python version used : 3 #&_# BEGIN OF TEST : tests/mask_test #&_# /home/admin/workarea/git/Velours/python/tests/mask_test.py Test mask-detection python version used : 3 ############################### TEST memory used ################################ free memory at begining : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1455538272857666 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Apr 23 11:20:28 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 10814 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 /home/admin/workarea/git/Velours/python/tests/python_tests.py:11: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses import imp 2025-04-23 11:20:32.341004: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-23 11:20:32.371041: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-23 11:20:32.373216: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f59f4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:20:32.373276: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-23 11:20:32.377665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-23 11:20:32.623397: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3e681fb0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:20:32.623443: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-23 11:20:32.624447: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:32.624884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:32.627557: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:32.630122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:32.630553: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:32.633398: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:32.634854: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:32.640012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:32.641642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:32.641735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:32.642545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:20:32.642562: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:20:32.642572: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:20:32.644007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param To do loadFromThcl(), then load ParamDescType : thcl454 thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 2025-04-23 11:20:33.360681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:33.360814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:33.360839: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:33.360860: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:33.360880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:33.360899: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:33.360918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:33.360937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:33.362636: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:33.365566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:33.365687: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:33.365710: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:33.365731: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:33.365750: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:33.365769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:33.365788: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:33.365808: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:33.367492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:33.367541: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:20:33.367553: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:20:33.367564: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:20:33.370085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10023 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-23 11:20:43.765858: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:43.945458: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (480, 640, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 640.00000 nb d'objets trouves : 5 Detection mask done ! Trying to reset tf kernel 1620689 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 1343 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 6238 list_Values should be empty [] To do loadFromThcl(), then load ParamDescType : thcl454 Catched exception ! Connect or reconnect ! thcls : [{'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3473 ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0007958412170410156 nb_pixel_total : 15552 time to create 1 rle with old method : 0.03654932975769043 length of segment : 256 time for calcul the mask position with numpy : 0.002761363983154297 nb_pixel_total : 144302 time to create 1 rle with old method : 0.3342428207397461 length of segment : 373 time for calcul the mask position with numpy : 0.00018143653869628906 nb_pixel_total : 5645 time to create 1 rle with old method : 0.013497114181518555 length of segment : 49 time for calcul the mask position with numpy : 0.00028014183044433594 nb_pixel_total : 14876 time to create 1 rle with old method : 0.03295612335205078 length of segment : 146 time for calcul the mask position with numpy : 8.249282836914062e-05 nb_pixel_total : 1982 time to create 1 rle with old method : 0.004769086837768555 length of segment : 40 time spent for convertir_results : 1.4946997165679932 time spend for datou_step_exec : 22.69840097427368 time spend to save output : 5.7697296142578125e-05 total time spend for step 1 : 22.698458671569824 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Number saved : None batch 1 Loaded 3331 chid ids of type : 445 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 608 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.011697053909301758 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'957285035': [[(957285035, 492601069, 445, 0, 186, 22, 282, 0.99548495, [(140, 26, 6), (135, 27, 15), (133, 28, 18), (131, 29, 22), (126, 30, 28), (10, 31, 1), (120, 31, 35), (8, 32, 13), (27, 32, 3), (115, 32, 41), (7, 33, 52), (109, 33, 48), (6, 34, 70), (103, 34, 55), (5, 35, 154), (4, 36, 155), (3, 37, 156), (3, 38, 156), (3, 39, 156), (2, 40, 157), (2, 41, 157), (2, 42, 157), (2, 43, 157), (2, 44, 157), (2, 45, 157), (1, 46, 158), (1, 47, 158), (1, 48, 158), (1, 49, 157), (1, 50, 157), (1, 51, 156), (1, 52, 156), (1, 53, 155), (1, 54, 154), (1, 55, 152), (1, 56, 149), (1, 57, 145), (1, 58, 141), (1, 59, 136), (1, 60, 133), (1, 61, 130), (1, 62, 127), (1, 63, 126), (1, 64, 124), (1, 65, 123), (1, 66, 121), (1, 67, 120), (1, 68, 118), (1, 69, 117), (1, 70, 116), (1, 71, 115), (1, 72, 114), (1, 73, 113), (1, 74, 112), (1, 75, 111), (1, 76, 110), (1, 77, 108), (1, 78, 108), (1, 79, 107), (1, 80, 106), (1, 81, 105), (2, 82, 104), (2, 83, 103), (2, 84, 103), (2, 85, 102), (2, 86, 102), (2, 87, 101), (2, 88, 100), (2, 89, 99), (2, 90, 99), (2, 91, 98), (2, 92, 97), (2, 93, 96), (2, 94, 95), (2, 95, 93), (2, 96, 91), (2, 97, 90), (2, 98, 89), (2, 99, 87), (2, 100, 86), (2, 101, 86), (2, 102, 85), (2, 103, 84), (2, 104, 83), (2, 105, 83), (2, 106, 82), (2, 107, 81), (2, 108, 80), (2, 109, 80), (2, 110, 79), (2, 111, 78), (2, 112, 77), (2, 113, 76), (1, 114, 76), (1, 115, 75), (1, 116, 74), (1, 117, 73), (1, 118, 72), (1, 119, 71), (1, 120, 71), (1, 121, 70), (1, 122, 69), (1, 123, 69), (1, 124, 68), (1, 125, 68), (1, 126, 67), (1, 127, 67), (1, 128, 66), (1, 129, 66), (1, 130, 66), (1, 131, 65), (1, 132, 65), (1, 133, 64), (1, 134, 63), (1, 135, 63), (1, 136, 62), (1, 137, 61), (1, 138, 60), (1, 139, 60), (1, 140, 59), (1, 141, 58), (1, 142, 58), (1, 143, 57), (1, 144, 56), (1, 145, 56), (1, 146, 55), (1, 147, 54), (1, 148, 54), (1, 149, 53), (1, 150, 52), (1, 151, 52), (1, 152, 51), (1, 153, 50), (1, 154, 49), (1, 155, 48), (1, 156, 47), (1, 157, 46), (1, 158, 45), (1, 159, 45), (1, 160, 44), (1, 161, 43), (1, 162, 42), (1, 163, 41), (1, 164, 41), (1, 165, 40), (1, 166, 40), (1, 167, 39), (1, 168, 38), (1, 169, 37), (1, 170, 36), (1, 171, 35), (1, 172, 34), (1, 173, 34), (1, 174, 33), (1, 175, 33), (1, 176, 32), (1, 177, 32), (1, 178, 32), (1, 179, 32), (1, 180, 31), (1, 181, 31), (1, 182, 31), (1, 183, 30), (1, 184, 30), (1, 185, 30), (1, 186, 29), (1, 187, 29), (1, 188, 29), (1, 189, 28), (1, 190, 28), (1, 191, 27), (1, 192, 27), (1, 193, 26), (1, 194, 26), (1, 195, 26), (1, 196, 26), (1, 197, 26), (1, 198, 26), (1, 199, 26), (1, 200, 25), (1, 201, 25), (1, 202, 25), (1, 203, 25), (1, 204, 25), (1, 205, 25), (1, 206, 25), (1, 207, 25), (1, 208, 25), (1, 209, 25), (1, 210, 25), (1, 211, 25), (1, 212, 25), (1, 213, 25), (1, 214, 25), (1, 215, 25), (1, 216, 25), (1, 217, 25), (1, 218, 25), (1, 219, 25), (1, 220, 24), (1, 221, 24), (1, 222, 24), (1, 223, 24), (1, 224, 24), (1, 225, 24), (1, 226, 25), (1, 227, 25), (1, 228, 25), (2, 229, 24), (2, 230, 24), (2, 231, 24), (2, 232, 23), (2, 233, 23), (2, 234, 23), (2, 235, 23), (2, 236, 23), (2, 237, 23), (2, 238, 23), (2, 239, 23), (2, 240, 23), (2, 241, 23), (2, 242, 23), (2, 243, 23), (2, 244, 23), (2, 245, 23), (2, 246, 23), (2, 247, 23), (2, 248, 23), (2, 249, 24), (2, 250, 24), (2, 251, 23), (2, 252, 23), (2, 253, 23), (2, 254, 23), (2, 255, 23), (2, 256, 23), (2, 257, 23), (2, 258, 23), (2, 259, 23), (2, 260, 23), (2, 261, 23), (3, 262, 22), (3, 263, 22), (3, 264, 22), (3, 265, 22), (4, 266, 21), (4, 267, 21), (5, 268, 20), (5, 269, 20), (6, 270, 19), (7, 271, 17), (8, 272, 16), (8, 273, 16), (9, 274, 13), (11, 275, 9), (15, 276, 2)], ['16,276,8,273,2,261,2,229,1,228,1,114,2,113,2,82,1,81,1,46,3,37,8,32,20,32,21,33,58,33,59,34,75,34,76,35,102,35,114,33,120,31,130,30,135,27,145,26,152,29,158,35,158,48,154,54,141,58,128,61,119,67,105,81,103,86,96,94,89,98,81,109,71,119,65,132,60,138,52,151,45,158,40,166,34,172,29,188,26,193,25,200,25,226,24,232,24,270,23,273']), (957285035, 492601069, 445, 10, 588, 16, 410, 0.9913191, [(300, 34, 22), (275, 35, 97), (258, 36, 118), (247, 37, 133), (241, 38, 143), (235, 39, 153), (227, 40, 170), (185, 41, 228), (182, 42, 239), (179, 43, 250), (175, 44, 264), (172, 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(49, 171, 514), (49, 172, 514), (49, 173, 514), (48, 174, 515), (48, 175, 515), (47, 176, 516), (47, 177, 516), (47, 178, 516), (46, 179, 517), (45, 180, 517), (45, 181, 517), (44, 182, 518), (43, 183, 519), (43, 184, 518), (42, 185, 519), (41, 186, 520), (41, 187, 519), (40, 188, 520), (40, 189, 520), (39, 190, 520), (39, 191, 520), (38, 192, 520), (38, 193, 520), (38, 194, 519), (37, 195, 519), (37, 196, 518), (36, 197, 519), (36, 198, 518), (36, 199, 518), (35, 200, 518), (35, 201, 518), (35, 202, 517), (34, 203, 518), (34, 204, 517), (34, 205, 517), (34, 206, 517), (34, 207, 516), (33, 208, 517), (33, 209, 516), (33, 210, 516), (33, 211, 516), (33, 212, 515), (33, 213, 515), (33, 214, 514), (33, 215, 514), (32, 216, 515), (32, 217, 514), (32, 218, 514), (32, 219, 514), (32, 220, 513), (32, 221, 513), (31, 222, 513), (31, 223, 512), (31, 224, 512), (31, 225, 511), (31, 226, 510), (30, 227, 510), (30, 228, 509), (30, 229, 507), (30, 230, 506), (29, 231, 506), (29, 232, 504), (29, 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(483, 41, 11), (478, 42, 3)], ['480,42,478,42,477,36,470,29,468,24,465,21,460,20,458,18,457,18,454,14,453,14,449,11,450,8,462,8,463,9,468,9,469,10,474,10,477,7,489,7,490,8,505,8,506,9,518,9,519,8,522,8,523,9,527,9,528,10,531,10,532,11,540,11,543,13,546,13,546,18,545,19,542,19,541,20,536,20,535,21,528,21,520,25,518,27,513,29,502,38,500,38,497,39,493,41,483,41,482,40'])], 'temp/1745400028_1620143_957285035_a42482e51c93c8025d243dd179aee85b.jpg']} free memory after detection : begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 6238 error , can't release the memory or there are other process who occupe the free memory ERROR test release memory FAILED ############################### TEST detect object ################################ run mask_detect Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.21361112594604492 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Apr 23 11:20:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6238 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-23 11:20:57.064552: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-23 11:20:57.095270: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-23 11:20:57.097025: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f59f8000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:20:57.097085: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-23 11:20:57.100234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-23 11:20:57.245316: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3f7c4f00 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:20:57.245371: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-23 11:20:57.246186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:57.246565: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:57.250799: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:57.252862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:57.253214: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:57.260143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:57.261201: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:57.265626: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:57.266665: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:57.266768: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:57.267308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:20:57.267325: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:20:57.267335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:20:57.268177: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1371 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-23 11:20:57.387796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:57.387941: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:57.387964: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:57.387983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:57.387999: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:57.388017: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:57.388034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:57.388053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:57.388828: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:57.389950: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:20:57.390016: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:20:57.390036: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:20:57.390054: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:20:57.390072: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:20:57.390089: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:20:57.390106: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:20:57.390123: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:20:57.390857: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:20:57.390912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:20:57.390923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:20:57.390931: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:20:57.391726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1371 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-23 11:21:07.711329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:21:07.874140: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:21:09.109621: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.109691: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.116239: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.116292: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.167041: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.167105: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.06GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.209069: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.209118: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.258887: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.258948: W tensorflow/core/common_runtime/bfc_allocator.cc:245] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.15GiB with freed_by_count=0. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 2025-04-23 11:21:09.285455: W tensorflow/core/common_runtime/bfc_allocator.cc:311] Garbage collection: deallocate free memory regions (i.e., allocations) so that we can re-allocate a larger region to avoid OOM due to memory fragmentation. If you see this message frequently, you are running near the threshold of the available device memory and re-allocation may incur great performance overhead. You may try smaller batch sizes to observe the performance impact. Set TF_ENABLE_GPU_GARBAGE_COLLECTION=false if you'd like to disable this feature. local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (720, 1280, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 1280.00000 nb d'objets trouves : 4 Detection mask done ! Trying to reset tf kernel 1624273 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 4982 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 7037 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.0006623268127441406 nb_pixel_total : 16902 time to create 1 rle with old method : 0.05445408821105957 length of segment : 107 time for calcul the mask position with numpy : 0.02168440818786621 nb_pixel_total : 480743 time to create 1 rle with new method : 0.033521413803100586 length of segment : 632 time for calcul the mask position with numpy : 0.0005373954772949219 nb_pixel_total : 36642 time to create 1 rle with old method : 0.08435773849487305 length of segment : 133 time for calcul the mask position with numpy : 0.0001850128173828125 nb_pixel_total : 4793 time to create 1 rle with old method : 0.011681795120239258 length of segment : 51 time spent for convertir_results : 0.46887636184692383 time spend for datou_step_exec : 19.918554067611694 time spend to save output : 5.91278076171875e-05 total time spend for step 1 : 19.91861319541931 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 419 chid ids of type : 445 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.017687082290649414 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917855882': [[(917855882, 492601069, 445, 1092, 1280, 0, 108, 0.9988368, [(1205, 1, 58), (1165, 2, 105), (1159, 3, 113), (1149, 4, 124), (1113, 5, 161), (1100, 6, 174), (1097, 7, 177), (1095, 8, 179), (1095, 9, 179), (1095, 10, 179), (1095, 11, 179), (1095, 12, 179), (1095, 13, 179), (1095, 14, 178), (1095, 15, 178), (1095, 16, 178), (1095, 17, 178), (1095, 18, 177), (1095, 19, 177), (1095, 20, 177), (1095, 21, 177), (1095, 22, 177), (1095, 23, 178), (1095, 24, 178), (1095, 25, 178), (1095, 26, 179), (1095, 27, 179), (1095, 28, 180), (1095, 29, 181), (1095, 30, 182), (1095, 31, 183), (1095, 32, 183), (1095, 33, 184), (1095, 34, 184), (1096, 35, 183), (1096, 36, 183), (1096, 37, 184), (1097, 38, 183), (1097, 39, 183), (1097, 40, 183), (1098, 41, 182), (1098, 42, 182), (1098, 43, 182), (1099, 44, 181), (1099, 45, 181), (1099, 46, 181), (1100, 47, 180), (1100, 48, 180), (1101, 49, 179), (1101, 50, 179), (1102, 51, 178), (1102, 52, 178), (1103, 53, 177), (1103, 54, 177), (1104, 55, 176), (1104, 56, 176), (1104, 57, 176), (1104, 58, 176), (1105, 59, 175), (1105, 60, 175), (1105, 61, 175), (1105, 62, 175), (1105, 63, 175), (1106, 64, 174), (1106, 65, 174), (1106, 66, 174), (1106, 67, 174), (1106, 68, 174), (1106, 69, 174), (1106, 70, 174), (1106, 71, 174), (1106, 72, 174), (1106, 73, 174), (1107, 74, 173), (1107, 75, 173), (1107, 76, 173), (1107, 77, 173), (1107, 78, 173), (1107, 79, 173), (1108, 80, 172), (1108, 81, 172), (1109, 82, 171), (1110, 83, 170), (1110, 84, 170), (1111, 85, 169), (1112, 86, 168), (1113, 87, 166), (1114, 88, 165), (1115, 89, 164), (1117, 90, 162), (1120, 91, 159), (1138, 92, 141), (1146, 93, 133), (1154, 94, 125), (1167, 95, 112), (1177, 96, 102), (1183, 97, 95), (1185, 98, 93), (1187, 99, 90), (1188, 100, 55), (1264, 100, 12), (1190, 101, 50), (1191, 102, 46), (1194, 103, 40), (1197, 104, 34), (1202, 105, 25), (1207, 106, 16)], ['1222,106,1207,106,1206,105,1197,104,1191,102,1182,96,1176,95,1167,95,1166,94,1154,94,1153,93,1146,93,1145,92,1137,91,1120,91,1115,89,1110,84,1107,79,1106,73,1106,64,1104,55,1099,46,1095,34,1095,8,1100,6,1112,6,1113,5,1148,5,1149,4,1158,4,1165,2,1204,2,1205,1,1262,1,1269,2,1273,5,1273,13,1271,18,1271,22,1273,27,1277,31,1279,37,1279,86,1278,87,1278,96,1275,100,1264,100,1263,99,1243,99,1230,104']), (917855882, 492601069, 445, 52, 1128, 16, 668, 0.99774724, [(711, 22, 21), (925, 22, 47), (608, 23, 146), (894, 23, 103), (598, 24, 233), (850, 24, 158), (590, 25, 427), (582, 26, 444), (575, 27, 458), (569, 28, 466), (565, 29, 472), (560, 30, 480), (556, 31, 486), (550, 32, 495), (544, 33, 503), (538, 34, 512), (532, 35, 520), (527, 36, 527), (523, 37, 534), (518, 38, 541), (514, 39, 548), (510, 40, 554), (506, 41, 561), (503, 42, 566), (499, 43, 572), (496, 44, 577), (493, 45, 582), (491, 46, 585), (489, 47, 589), (487, 48, 592), (485, 49, 595), (483, 50, 598), (482, 51, 600), (481, 52, 602), (480, 53, 603), (479, 54, 605), (478, 55, 606), (477, 56, 607), (475, 57, 610), (474, 58, 611), (473, 59, 613), (472, 60, 614), (470, 61, 616), (469, 62, 618), (468, 63, 619), (466, 64, 621), (465, 65, 623), (464, 66, 624), (462, 67, 626), (461, 68, 628), (459, 69, 630), (458, 70, 631), (456, 71, 633), (455, 72, 635), (453, 73, 637), (452, 74, 638), (451, 75, 639), (450, 76, 640), (448, 77, 642), (447, 78, 643), (446, 79, 644), (445, 80, 645), (444, 81, 646), (442, 82, 648), (441, 83, 649), (440, 84, 650), (439, 85, 651), (438, 86, 652), (437, 87, 653), (436, 88, 654), (435, 89, 655), (434, 90, 656), (433, 91, 657), (432, 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['449,46,443,46,442,45,426,45,424,41,424,37,423,36,422,31,420,28,420,25,419,24,419,21,418,20,418,17,417,15,409,6,402,3,402,1,413,1,414,0,420,0,421,1,440,1,441,0,500,0,501,1,507,1,508,0,535,0,536,1,543,1,546,2,546,4,542,8,530,18,527,19,525,21,522,22,520,24,512,28,508,33,505,34,502,37,494,41,492,41,490,43,488,43,484,45,473,45,472,46'])], 'temp/1745400053_1620143_917855882_da0fa7b7e6b5b551fe26c0ba8713276d.jpg']} ############################### TEST POLYGON ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : mask_detect list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15019941329956055 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:mask_detect Wed Apr 23 11:21:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step mask_detect ! save_polygon : True begin detect begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 7037 max_wait_temp : 1 max_wait : 0 gpu_flag : 0 2025-04-23 11:21:18.433928: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-23 11:21:18.463178: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-23 11:21:18.465613: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f59f4000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:21:18.465702: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-23 11:21:18.474031: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-23 11:21:18.644340: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3fce44d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:21:18.644441: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-23 11:21:18.646268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:21:18.647030: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:21:18.650365: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:21:18.653250: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:21:18.653735: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:21:18.656150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:21:18.657346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:21:18.662074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:21:18.663576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:21:18.663678: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:21:18.664385: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:21:18.664402: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:21:18.664412: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:21:18.665928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6472 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) WARNING:tensorflow:From /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py:69: The name tf.keras.backend.set_session is deprecated. Please use tf.compat.v1.keras.backend.set_session instead. 2025-04-23 11:21:18.807618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:21:18.807720: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:21:18.807743: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:21:18.807765: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:21:18.807786: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:21:18.807807: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:21:18.807827: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:21:18.807849: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:21:18.809193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:21:18.810771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:21:18.810937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:21:18.810983: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:21:18.811018: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:21:18.811052: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:21:18.811104: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:21:18.811138: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:21:18.811172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:21:18.812652: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:21:18.812695: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:21:18.812706: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:21:18.812714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:21:18.814137: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6472 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) Using TensorFlow backend. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:396: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version. Instructions for updating: box_ind is deprecated, use box_indices instead WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:703: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. WARNING:tensorflow:From /home/admin/workarea/install/Mask_RCNN/model.py:729: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.cast` instead. Inside mask_sub_process Inside mask_detect About to load cache.load_thcl_param FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3473, 'mask_coco_origin', 16384, 25088, 'mask_coco_origin', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 3, 19, 10, 42, 21), datetime.datetime(2018, 3, 19, 10, 42, 21)) {'thcl': {'id': 454, 'mtr_user_id': 31, 'name': 'mask_coco_origin', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'photo_desc_type': 3473, 'type_classification': 'mask_rcnn', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}, 'list_hashtags': ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'], 'list_hashtags_csv': 'backgroud,person,bicycle,car,motorcycle,airplane,bus,train,truck,boat,trafficlight,firehydrant,stopsign,parkingmeter,bench,bird,cat,dog,horse,sheep,cow,elephant,bear,zebra,giraffe,backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sportsball,kite,baseballbat,baseballglove,skateboard,surfboard,tennisracket,bottle,wineglass,cup,fork,knife,spoon,bowl,banana,apple,sandwich,orange,broccoli,carrot,hotdog,pizza,donut,cake,chair,couch,pottedplant,bed,diningtable,toilet,tv,laptop,mouse,remote,keyboard,cellphone,microwave,oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddybear,hairdrier,toothbrush', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 445, 'svm_hashtag_type_desc': 3473, 'photo_desc_type': 3473, 'pb_hashtag_id_or_classifier': 0} list_class_names : ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] Configurations: BACKBONE resnet101 BACKBONE_SHAPES [[160 160] [ 80 80] [ 40 40] [ 20 20] [ 10 10]] BACKBONE_STRIDES [4, 8, 16, 32, 64] BATCH_SIZE 1 BBOX_STD_DEV [0.1 0.1 0.2 0.2] DETECTION_MAX_INSTANCES 100 DETECTION_MIN_CONFIDENCE 0.3 DETECTION_NMS_THRESHOLD 0.3 GPU_COUNT 1 IMAGES_PER_GPU 1 IMAGE_MAX_DIM 640 IMAGE_MIN_DIM 640 IMAGE_PADDING True IMAGE_SHAPE [640 640 3] LEARNING_MOMENTUM 0.9 LEARNING_RATE 0.001 LOSS_WEIGHTS {'rpn_class_loss': 1.0, 'rpn_bbox_loss': 1.0, 'mrcnn_class_loss': 1.0, 'mrcnn_bbox_loss': 1.0, 'mrcnn_mask_loss': 1.0} MASK_POOL_SIZE 14 MASK_SHAPE [28, 28] MAX_GT_INSTANCES 100 MEAN_PIXEL [123.7 116.8 103.9] MINI_MASK_SHAPE (56, 56) NAME mask_coco_origin NUM_CLASSES 81 POOL_SIZE 7 POST_NMS_ROIS_INFERENCE 1000 POST_NMS_ROIS_TRAINING 2000 ROI_POSITIVE_RATIO 0.33 RPN_ANCHOR_RATIOS [0.5, 1, 2] RPN_ANCHOR_SCALES (16, 32, 64, 128, 256) RPN_ANCHOR_STRIDE 1 RPN_BBOX_STD_DEV [0.1 0.1 0.2 0.2] RPN_NMS_THRESHOLD 0.7 RPN_TRAIN_ANCHORS_PER_IMAGE 256 STEPS_PER_EPOCH 1000 TRAIN_ROIS_PER_IMAGE 200 USE_MINI_MASK True USE_RPN_ROIS True VALIDATION_STEPS 50 WEIGHT_DECAY 0.0001 model_param file didn't exist model_name : mask_coco_origin model_type : mask_rcnn list file need : ['mask_model.h5'] file exist in s3 : ['mask_model.h5'] file manque in s3 : [] 2025-04-23 11:21:32.704433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:21:32.905752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 local folder : /data/models_weight/mask_coco_origin /data/models_weight/mask_coco_origin/mask_model.h5 size_local : 257557808 size in s3 : 257557808 create time local : 2021-08-09 05:27:17 create time in s3 : 2021-08-06 19:45:17 mask_model.h5 already exist and didn't need to update list_images length : 1 NEW PHOTO Processing 1 images image shape: (2448, 2448, 3) min: 0.00000 max: 255.00000 molded_images shape: (1, 640, 640, 3) min: -123.70000 max: 151.10000 image_metas shape: (1, 89) min: 0.00000 max: 2448.00000 nb d'objets trouves : 1 Detection mask done ! Trying to reset tf kernel 1626945 begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 5525 tf kernel not reseted sub process len(results) : 1 len(list_Values) 0 None max_time_sub_proc : 3600 parent process len(results) : 1 len(list_Values) 0 process is alive finish correctly or not : True after detect begin to check gpu status inside check gpu memory l 3610 free memory gpu now : 10814 list_Values should be empty [] ['backgroud', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'trafficlight', 'firehydrant', 'stopsign', 'parkingmeter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sportsball', 'kite', 'baseballbat', 'baseballglove', 'skateboard', 'surfboard', 'tennisracket', 'bottle', 'wineglass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hotdog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'pottedplant', 'bed', 'diningtable', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cellphone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddybear', 'hairdrier', 'toothbrush'] time for calcul the mask position with numpy : 0.40011119842529297 nb_pixel_total : 3689234 time to create 1 rle with new method : 0.6924567222595215 length of segment : 2038 time spent for convertir_results : 2.6385059356689453 time spend for datou_step_exec : 26.71978449821472 time spend to save output : 3.1948089599609375e-05 total time spend for step 1 : 26.71981644630432 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False eke 12-6-18 : saveMask need to be cleaned for new output ! Catched exception ! Connect or reconnect ! Number saved : None batch 1 Loaded 721 chid ids of type : 445 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++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014600276947021484 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'917877156': [[(917877156, 492601069, 445, 7, 2268, 122, 2241, 0.9850354, [(523, 124, 445), (1147, 124, 280), (502, 125, 949), (481, 126, 993), (461, 127, 1035), (443, 128, 1091), (427, 129, 1148), (411, 130, 1178), (396, 131, 1201), (382, 132, 1223), (370, 133, 1243), (367, 134, 1253), (364, 135, 1264), (362, 136, 1272), (359, 137, 1282), (357, 138, 1290), (354, 139, 1299), (352, 140, 1307), (350, 141, 1314), (347, 142, 1322), (345, 143, 1328), (343, 144, 1334), (341, 145, 1340), (340, 146, 1344), (338, 147, 1350), (336, 148, 1355), (334, 149, 1361), (333, 150, 1365), (331, 151, 1370), (329, 152, 1375), (328, 153, 1379), (326, 154, 1384), (325, 155, 1388), (324, 156, 1392), (322, 157, 1396), (321, 158, 1400), (320, 159, 1403), (318, 160, 1408), (317, 161, 1411), (316, 162, 1415), (315, 163, 1419), (314, 164, 1422), (312, 165, 1427), (311, 166, 1431), (309, 167, 1435), (308, 168, 1438), (307, 169, 1442), (305, 170, 1446), (303, 171, 1450), (302, 172, 1454), (300, 173, 1458), (299, 174, 1462), (297, 175, 1466), (295, 176, 1471), (293, 177, 1476), (291, 178, 1480), (289, 179, 1485), (287, 180, 1490), (284, 181, 1497), (281, 182, 1503), (279, 183, 1508), (276, 184, 1515), (274, 185, 1520), (271, 186, 1527), (268, 187, 1534), (265, 188, 1541), (263, 189, 1547), (260, 190, 1554), (257, 191, 1561), (254, 192, 1569), (252, 193, 1577), (249, 194, 1586), (246, 195, 1596), (243, 196, 1605), (240, 197, 1614), (238, 198, 1622), (235, 199, 1631), (232, 200, 1640), (229, 201, 1648), (226, 202, 1657), (223, 203, 1666), (220, 204, 1675), (217, 205, 1683), (214, 206, 1691), (211, 207, 1696), (208, 208, 1701), (206, 209, 1705), (205, 210, 1707), (203, 211, 1711), (201, 212, 1715), (199, 213, 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'temp/1745400074_1620143_917877156_a9c2d4b99270c9302def4ed40606e685.jpg']} nb pixel non reg : 3692295 nb pixel common : 3686352 proportion of common points : 0.9983904319671099 #&_# TEST FAILED #&_# : tests/mask_test #&_# #&_# END OF TEST #&_# : tests/mask_test #&_# #&_# BEGIN OF TEST : tests/datou_test #&_# /home/admin/workarea/git/Velours/python/tests/datou_test.py Datou All Test python version used : 3 ############################### TEST sam ################################ TEST SAM Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : sam list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18049335479736328 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:sam Wed Apr 23 11:21:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step sam ! Inside sam : nb paths : 1 (640, 960, 3) time for calcul the mask position with numpy : 0.0020589828491210938 nb_pixel_total : 5613 time to create 1 rle with old method : 0.013374090194702148 time for calcul the mask position with numpy : 0.0015530586242675781 nb_pixel_total : 16296 time to create 1 rle with old method : 0.0372471809387207 time for calcul the mask position with numpy : 0.002084493637084961 nb_pixel_total : 83515 time to create 1 rle with old method : 0.1918952465057373 time for calcul the mask position with numpy : 0.0015344619750976562 nb_pixel_total : 13960 time to create 1 rle with old method : 0.035645246505737305 time for calcul the mask position with numpy : 0.001664876937866211 nb_pixel_total : 38839 time to create 1 rle with old method : 0.08901572227478027 time for calcul the mask position with numpy : 0.0014543533325195312 nb_pixel_total : 3781 time to create 1 rle with old method : 0.008747339248657227 time for calcul the mask position with numpy : 0.00140380859375 nb_pixel_total : 16395 time to create 1 rle with old method : 0.03644132614135742 time for calcul the mask position with numpy : 0.001416921615600586 nb_pixel_total : 5523 time to create 1 rle with old method : 0.012749433517456055 time for calcul the mask position with numpy : 0.0013706684112548828 nb_pixel_total : 2938 time to create 1 rle with old method : 0.0067386627197265625 time for calcul the mask position with numpy : 0.0014338493347167969 nb_pixel_total : 10819 time to create 1 rle with old method : 0.023561716079711914 time for calcul the mask position with numpy : 0.0015728473663330078 nb_pixel_total : 29453 time to create 1 rle with old method : 0.06605935096740723 time for calcul the mask position with numpy : 0.001482248306274414 nb_pixel_total : 16699 time to create 1 rle with old method : 0.036839962005615234 time for calcul the mask position with numpy : 0.0014939308166503906 nb_pixel_total : 4273 time to create 1 rle with old method : 0.010059595108032227 time for calcul the mask position with numpy : 0.001466989517211914 nb_pixel_total : 3924 time to create 1 rle with old method : 0.009500741958618164 time for calcul the mask position with numpy : 0.0014827251434326172 nb_pixel_total : 1226 time to create 1 rle with old method : 0.0030188560485839844 time for calcul the mask position with numpy : 0.0015926361083984375 nb_pixel_total : 2452 time to create 1 rle with old method : 0.005942344665527344 time for calcul the mask position with numpy : 0.0014414787292480469 nb_pixel_total : 6640 time to create 1 rle with old method : 0.01881265640258789 time for calcul the mask position with numpy : 0.0017571449279785156 nb_pixel_total : 13093 time to create 1 rle with old method : 0.030396461486816406 time for calcul the mask position with numpy : 0.0015611648559570312 nb_pixel_total : 12876 time to create 1 rle with old method : 0.03070831298828125 time for calcul the mask position with numpy : 0.0014801025390625 nb_pixel_total : 2078 time to create 1 rle with old method : 0.004889965057373047 time for calcul the mask position with numpy : 0.0014715194702148438 nb_pixel_total : 7649 time to create 1 rle with old method : 0.017801523208618164 time for calcul the mask position with numpy : 0.0013880729675292969 nb_pixel_total : 850 time to create 1 rle with old method : 0.0021915435791015625 time for calcul the mask position with numpy : 0.0014870166778564453 nb_pixel_total : 9837 time to create 1 rle with old method : 0.02342057228088379 time for calcul the mask position with numpy : 0.0015044212341308594 nb_pixel_total : 2776 time to create 1 rle with old method : 0.0068569183349609375 time for calcul the mask position with numpy : 0.0015976428985595703 nb_pixel_total : 4272 time to create 1 rle with old method : 0.010734796524047852 time for calcul the mask position with numpy : 0.0016472339630126953 nb_pixel_total : 866 time to create 1 rle with old method : 0.0023076534271240234 time for calcul the mask position with numpy : 0.0015485286712646484 nb_pixel_total : 1686 time to create 1 rle with old method : 0.004104137420654297 time for calcul the mask position with numpy : 0.0015218257904052734 nb_pixel_total : 8611 time to create 1 rle with old method : 0.023360013961791992 time for calcul the mask position with numpy : 0.0015451908111572266 nb_pixel_total : 3532 time to create 1 rle with old method : 0.008540630340576172 time for calcul the mask position with numpy : 0.0015132427215576172 nb_pixel_total : 3325 time to create 1 rle with old method : 0.007945537567138672 time for calcul the mask position with numpy : 0.0014982223510742188 nb_pixel_total : 2780 time to create 1 rle with old method : 0.006681680679321289 time for calcul the mask position with numpy : 0.0015749931335449219 nb_pixel_total : 14930 time to create 1 rle with old method : 0.03783464431762695 time for calcul the mask position with numpy : 0.0018017292022705078 nb_pixel_total : 2449 time to create 1 rle with old method : 0.005917549133300781 time for calcul the mask position with numpy : 0.0017747879028320312 nb_pixel_total : 2727 time to create 1 rle with old method : 0.008690834045410156 time for calcul the mask position with numpy : 0.001901388168334961 nb_pixel_total : 1021 time to create 1 rle with old method : 0.0026018619537353516 time for calcul the mask position with numpy : 0.001596689224243164 nb_pixel_total : 11941 time to create 1 rle with old method : 0.028393030166625977 time for calcul the mask position with numpy : 0.0016760826110839844 nb_pixel_total : 5404 time to create 1 rle with old method : 0.012910127639770508 time for calcul the mask position with numpy : 0.0014712810516357422 nb_pixel_total : 3923 time to create 1 rle with old method : 0.009496688842773438 time for calcul the mask position with numpy : 0.0014646053314208984 nb_pixel_total : 4136 time to create 1 rle with old method : 0.010105133056640625 time for calcul the mask position with numpy : 0.00148773193359375 nb_pixel_total : 10624 time to create 1 rle with old method : 0.025815486907958984 time for calcul the mask position with numpy : 0.0014996528625488281 nb_pixel_total : 1648 time to create 1 rle with old method : 0.003978729248046875 time for calcul the mask position with numpy : 0.0014414787292480469 nb_pixel_total : 1253 time to create 1 rle with old method : 0.002990245819091797 time for calcul the mask position with numpy : 0.001439809799194336 nb_pixel_total : 594 time to create 1 rle with old method : 0.0015032291412353516 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 343 time to create 1 rle with old method : 0.0008561611175537109 time for calcul the mask position with numpy : 0.0014903545379638672 nb_pixel_total : 4176 time to create 1 rle with old method : 0.010091543197631836 time for calcul the mask position with numpy : 0.0016334056854248047 nb_pixel_total : 27706 time to create 1 rle with old method : 0.06584024429321289 time for calcul the mask position with numpy : 0.0014977455139160156 nb_pixel_total : 1672 time to create 1 rle with old method : 0.00412750244140625 time for calcul the mask position with numpy : 0.0016248226165771484 nb_pixel_total : 901 time to create 1 rle with old method : 0.002427816390991211 time for calcul the mask position with numpy : 0.0014438629150390625 nb_pixel_total : 2398 time to create 1 rle with old method : 0.005827188491821289 time for calcul the mask position with numpy : 0.001455545425415039 nb_pixel_total : 2324 time to create 1 rle with old method : 0.005563497543334961 time for calcul the mask position with numpy : 0.001493215560913086 nb_pixel_total : 2028 time to create 1 rle with old method : 0.004758596420288086 time for calcul the mask position with numpy : 0.0014281272888183594 nb_pixel_total : 577 time to create 1 rle with old method : 0.0014379024505615234 time for calcul the mask position with numpy : 0.0014324188232421875 nb_pixel_total : 2417 time to create 1 rle with old method : 0.005939483642578125 time for calcul the mask position with numpy : 0.001474142074584961 nb_pixel_total : 337 time to create 1 rle with old method : 0.0008683204650878906 time for calcul the mask position with numpy : 0.0014374256134033203 nb_pixel_total : 3097 time to create 1 rle with old method : 0.007344722747802734 time for calcul the mask position with numpy : 0.001421213150024414 nb_pixel_total : 585 time to create 1 rle with old method : 0.0014765262603759766 time for calcul the mask position with numpy : 0.001428365707397461 nb_pixel_total : 1206 time to create 1 rle with old method : 0.002953052520751953 time for calcul the mask position with numpy : 0.001531362533569336 nb_pixel_total : 16694 time to create 1 rle with old method : 0.03977799415588379 time for calcul the mask position with numpy : 0.0014808177947998047 nb_pixel_total : 1055 time to create 1 rle with old method : 0.002597332000732422 time for calcul the mask position with numpy : 0.0014193058013916016 nb_pixel_total : 1075 time to create 1 rle with old method : 0.002700328826904297 time for calcul the mask position with numpy : 0.0014164447784423828 nb_pixel_total : 692 time to create 1 rle with old method : 0.0016949176788330078 time for calcul the mask position with numpy : 0.0014955997467041016 nb_pixel_total : 9660 time to create 1 rle with old method : 0.02279829978942871 time for calcul the mask position with numpy : 0.0015208721160888672 nb_pixel_total : 8607 time to create 1 rle with old method : 0.020119667053222656 time for calcul the mask position with numpy : 0.001405954360961914 nb_pixel_total : 1744 time to create 1 rle with old method : 0.004203319549560547 time for calcul the mask position with numpy : 0.0016644001007080078 nb_pixel_total : 38749 time to create 1 rle with old method : 0.09395933151245117 time for calcul the mask position with numpy : 0.0014891624450683594 nb_pixel_total : 1513 time to create 1 rle with old method : 0.003634214401245117 time for calcul the mask position with numpy : 0.0014357566833496094 nb_pixel_total : 1328 time to create 1 rle with old method : 0.003143787384033203 time for calcul the mask position with numpy : 0.0014164447784423828 nb_pixel_total : 8449 time to create 1 rle with old method : 0.0194549560546875 time for calcul the mask position with numpy : 0.0014264583587646484 nb_pixel_total : 9086 time to create 1 rle with old method : 0.02109217643737793 time for calcul the mask position with numpy : 0.0014107227325439453 nb_pixel_total : 267 time to create 1 rle with old method : 0.0006127357482910156 time for calcul the mask position with numpy : 0.001341104507446289 nb_pixel_total : 722 time to create 1 rle with old method : 0.0018227100372314453 time for calcul the mask position with numpy : 0.0013437271118164062 nb_pixel_total : 1309 time to create 1 rle with old method : 0.0032558441162109375 time for calcul the mask position with numpy : 0.0013630390167236328 nb_pixel_total : 971 time to create 1 rle with old method : 0.002297639846801758 time for calcul the mask position with numpy : 0.0013799667358398438 nb_pixel_total : 3166 time to create 1 rle with old method : 0.007287263870239258 time for calcul the mask position with numpy : 0.0014078617095947266 nb_pixel_total : 1125 time to create 1 rle with old method : 0.0026721954345703125 time for calcul the mask position with numpy : 0.0014536380767822266 nb_pixel_total : 616 time to create 1 rle with old method : 0.0015418529510498047 time for calcul the mask position with numpy : 0.0014390945434570312 nb_pixel_total : 18480 time to create 1 rle with old method : 0.04177498817443848 time for calcul the mask position with numpy : 0.0013422966003417969 nb_pixel_total : 248 time to create 1 rle with old method : 0.0006227493286132812 time for calcul the mask position with numpy : 0.0014455318450927734 nb_pixel_total : 1486 time to create 1 rle with old method : 0.0035283565521240234 time for calcul the mask position with numpy : 0.0013260841369628906 nb_pixel_total : 970 time to create 1 rle with old method : 0.002301454544067383 time for calcul the mask position with numpy : 0.0013489723205566406 nb_pixel_total : 735 time to create 1 rle with old method : 0.0018398761749267578 time for calcul the mask position with numpy : 0.0014386177062988281 nb_pixel_total : 5014 time to create 1 rle with old method : 0.011641502380371094 time for calcul the mask position with numpy : 0.0013282299041748047 nb_pixel_total : 222 time to create 1 rle with old method : 0.0005564689636230469 time for calcul the mask position with numpy : 0.0013813972473144531 nb_pixel_total : 1632 time to create 1 rle with old method : 0.0037915706634521484 time for calcul the mask position with numpy : 0.001394510269165039 nb_pixel_total : 298 time to create 1 rle with old method : 0.0008215904235839844 time for calcul the mask position with numpy : 0.0013513565063476562 nb_pixel_total : 876 time to create 1 rle with old method : 0.0020630359649658203 time for calcul the mask position with numpy : 0.0013492107391357422 nb_pixel_total : 7500 time to create 1 rle with old method : 0.016597270965576172 time for calcul the mask position with numpy : 0.0013301372528076172 nb_pixel_total : 1442 time to create 1 rle with old method : 0.0032699108123779297 time for calcul the mask position with numpy : 0.001310110092163086 nb_pixel_total : 595 time to create 1 rle with old method : 0.001371622085571289 time for calcul the mask position with numpy : 0.0013344287872314453 nb_pixel_total : 917 time to create 1 rle with old method : 0.0022835731506347656 time for calcul the mask position with numpy : 0.0014452934265136719 nb_pixel_total : 1440 time to create 1 rle with old method : 0.003507375717163086 time for calcul the mask position with numpy : 0.0014500617980957031 nb_pixel_total : 892 time to create 1 rle with old method : 0.0021555423736572266 time for calcul the mask position with numpy : 0.0015211105346679688 nb_pixel_total : 2199 time to create 1 rle with old method : 0.00543522834777832 time for calcul the mask position with numpy : 0.0015125274658203125 nb_pixel_total : 947 time to create 1 rle with old method : 0.0023479461669921875 time for calcul the mask position with numpy : 0.0014455318450927734 nb_pixel_total : 475 time to create 1 rle with old method : 0.0012443065643310547 time for calcul the mask position with numpy : 0.0014836788177490234 nb_pixel_total : 1614 time to create 1 rle with old method : 0.004194974899291992 time for calcul the mask position with numpy : 0.001569509506225586 nb_pixel_total : 883 time to create 1 rle with old method : 0.0022754669189453125 time for calcul the mask position with numpy : 0.0015769004821777344 nb_pixel_total : 1197 time to create 1 rle with old method : 0.003127574920654297 time for calcul the mask position with numpy : 0.0015330314636230469 nb_pixel_total : 830 time to create 1 rle with old method : 0.0021185874938964844 batch 1 Loaded 99 chid ids of type : 4677 Number RLEs to save : 9418 TO DO : save crop sub photo not yet done ! Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : sam we use saveGeneral [1189321094] Looping around the photos to save general results len do output : 1 /1189321094Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4573', None, None, None, None, None, None, None, None) ('4573', None, '1189321094', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.014526605606079102 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.113753318786621 time spend to save output : 0.0150909423828125 total time spend for step 1 : 11.128844261169434 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1189321094': [[, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'temp/1745400107_1620143_1189321094_9626af7f95d010f2a4fd524688d4ea22_76896585.png']} nb_objects detect : 99 ############################### TEST frcnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : frcnn list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14377641677856445 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:frcnn Wed Apr 23 11:21:58 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! To loadFromThcl() model_param file didn't exist model_name : detection_plaque_valcor_010622 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/detection_plaque_valcor_010622 /data/models_weight/detection_plaque_valcor_010622/caffemodel size_local : 349723073 size in s3 : 349723073 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:56 caffemodel already exist and didn't need to update /data/models_weight/detection_plaque_valcor_010622/test.prototxt size_local : 7163 size in s3 : 7163 create time local : 2022-07-12 14:12:27 create time in s3 : 2022-06-01 15:05:55 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/detection_plaque_valcor_010622/test.prototxt caffemodel : /data/models_weight/detection_plaque_valcor_010622/caffemodel Loaded network /data/models_weight/detection_plaque_valcor_010622/caffemodel About to compute detect_faster_rcnn : len(args) : 1 Inside frcnn step exec : nb paths : 1 image_path : temp/1745400118_1620143_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg image_size (600, 800, 3) [[[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] [[ 4 6 6] [ 5 7 7] [ 6 8 8] ... [207 215 214] [206 214 213] [206 214 213]] ... [[ 14 16 16] [ 13 15 15] [ 11 13 13] ... [198 206 205] [198 206 205] [198 206 205]] [[ 16 18 18] [ 14 16 16] [ 11 13 13] ... [206 214 213] [206 214 213] [206 214 213]] [[ 13 15 15] [ 12 14 14] [ 9 11 11] ... [210 218 217] [210 218 217] [210 218 217]]] Detection took 0.071s for 300 object proposals len de result frcnn : 1 time spend for datou_step_exec : 2.4627492427825928 time spend to save output : 6.198883056640625e-05 total time spend for step 1 : 2.462811231613159 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False Inside saveFrcnn : final : True verbose : False threshold to save the result : 0.1 Warning : no hashtag_ids to insert in the database final : True begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014153480529785156 [917754606] Looping around the photos to save general results len do output : 1 /0 before output type Managing all output in save final without adding information in the mtr_datou_result ('4184', None, None, None, None, None, None, None, None) ('4184', None, '917754606', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.012288093566894531 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {0: [[(0, 493029425, 4370, 374, 430, 293, 317, 0.0638407, None), (0, 493029425, 4370, 382, 552, 297, 344, 0.05221273, None), (0, 493029425, 4370, 345, 468, 272, 320, 0.012271108, None)], 'temp/1745400118_1620143_917754606_35f3c9ae49686a6be16030c6ec25c9ee.jpg']} ############################### TEST thcl ################################ TEST THCL Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1 thcl is not linked in the step_by_step architecture ! WARNING : step 2 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.08974409103393555 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Wed Apr 23 11:22:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 355 time to import caffe and check if the image exist : 0.009189128875732422 time to convert the images to numpy array : 0.0009717941284179688 total time to convert the images to numpy array : 0.010607719421386719 list photo_ids error: [] list photo_ids correct : [916235064] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 355 To do loadFromThcl(), then load ParamDescType : thcl355 thcls : [{'id': 355, 'mtr_user_id': 31, 'name': 'car_360_1027', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 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'506302,506374,506399,506192,506205,506350,506052,506295,506066,506117,506065,506125,506387,506381,506349,506328,506377,506286,506124,506172,506206,506178,506371,506076,506114,506329,506122,506220,506174,506224,506232,506234,506173,506181,506323,506326,506376,506048,506400,506179,506311,506325,506402,506051,506294,506318,506303,506175,506099,506061,506337,506250,506082,506166,506133,506308,506078,506340,506310,506100,506121,506070,506218,506227,506272,506147,506160,506265,506202,506222,506093,506257,506208,506344,506077,506395,506094,506219,506298,506339,506343,506365,506200,506348,506198,506385,506239,506236,506391,506087,506342,506149,506184,506393,506203,506280,506216,506403,506355,506332,506259,506401,506357,506324,506098,506315,506335,506088,506046,506185,506171,506080,506345,506347,506067,506233,506225,506312,506278,506300,506258,506182,506226,506262,506146,506113,506108,506297,506322,506143,506363,506073,506154,506313,506189,506197,506162,506249,506139,506237,506336,506084,506109,506106,506045,506392,506247,506316,506201,506353,506305,506050,506145,506362,506101,506128,506044,506317,506074,506134,506196,506194,506285,506177,506240,506282,506396,506281,506264,506276,506144,506069,506091,506081,506168,506291,506238,506072,506085,506235,506193,506268,506148,506356,506386,506229,506256,506187,506110,506304,506115,506214,506334,506289,506361,506366,506204,506190,506188,506307,506055,506389,506364,506279,506241,506057,506063,506320,506212,506263,506394,506306,506260,506309,506221,506155,506176,506398,506360,506210,506341,506209,506170,506097,506119,506163,506092,506267,506246,506047,506296,506058,506269,506378,506123,506271,506277,506207,506141,506390,506314,506299,506075,506183,506157,506228,506255,506358,506053,506060,506382,506217,506290,506230,506186,506213,506248,506354,506245,506104,506111,506054,506068,506156,506102,506191,506158,506159,506153,506107,506056,506131,506165,506370,506161,506242,506327,506253,506330,506243,506231,506096,506331,506062,506195,506369,506384,506071,506116,506164,506090,506397,506273,506338,506140,506136,506086,506083,506275,506283,506142,506383,506380,506129,506368,506130,506367,506292,506064,506138,506167,506223,506351,506079,506132,506293,506089,506095,506120,506388,506211,506274,506321,506150,506169,506049,506379,506252,506112,506199,506287,506266,506118,506103,506301,506105,506137,506352,506333,506180,506254,506375,506270,506319,506288,506244,506284,506059,506261,506372,506127,506359,506135,506215,506151,506251,506152,506126,506373,506346', 'photo_hashtag_type': 332, 'photo_desc_type': 3390, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3390 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) To loadFromThcl() : net_3390 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6717 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3390, 'car_360_1027', 16384, 25088, 'car_360_1027', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2017, 10, 28, 12, 29, 27), datetime.datetime(2017, 10, 28, 12, 29, 27)) None mean_file_type : mean_file_path : prototxt_file_path : model : car_360_1027 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : car_360_1027 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/car_360_1027 /data/models_weight/car_360_1027/caffemodel size_local : 542944640 size in s3 : 542944640 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 caffemodel already exist and didn't need to update /data/models_weight/car_360_1027/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy_fc.prototxt size_local : 1132 size in s3 : 1132 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/deploy.prototxt size_local : 5654 size in s3 : 5654 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:42 deploy.prototxt already exist and didn't need to update /data/models_weight/car_360_1027/mean.npy size_local : 1572944 size in s3 : 1572944 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:55 mean.npy already exist and didn't need to update /data/models_weight/car_360_1027/synset_words.txt size_local : 13687 size in s3 : 13687 create time local : 2021-08-09 05:28:34 create time in s3 : 2021-08-06 17:57:43 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/car_360_1027/deploy.prototxt caffemodel_filename : /data/models_weight/car_360_1027/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 6717 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 0.01910257339477539 time used to do the prediction : 0.0626828670501709 save descriptor for thcl : 355 time to traite the descriptors : 0.06441259384155273 Catched exception ! Connect or reconnect ! storage_type for insertDescriptorsMulti : 1 To insert : 916235064 time to insert the descriptors : 0.7786290645599365 Inside saveOutput : final : False verbose : False time used to find the portfolios of the photos SAVE THCL : begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 0 time used for this insertion : 8.821487426757812e-06 save missing photos in datou_result : time spend for datou_step_exec : 6.278076887130737 time spend to save output : 1.5214104652404785 total time spend for step 1 : 7.799487352371216 step2:argmax Wed Apr 23 11:22:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 355 Inside saveOutput : final : True verbose : False photo_id : 916235064 output[photo_id] : [('916235064', 'c15_1027_gao__port_506055', 0.017714879, 332, '355'), 'temp/1745400121_1620143_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.009676218032836914 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.0117950439453125 len list_finale : 1, len picture : 1 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.014488935470581055 saving photo_ids in datou_result photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 6.198883056640625e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0003323554992675781 time spend to save output : 0.036347150802612305 total time spend for step 2 : 0.03667950630187988 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'916235064': [('916235064', 'c15_1027_gao__port_506055', 0.017714879, 332, '355'), 'temp/1745400121_1620143_916235064_6293d1bb790dc6902450e7c572b7d10b.jpg']} ############################### TEST tfhub2 ################################ TEST TFHUB2 ######################## test with use_multi_inputs=0 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12835 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12836 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.3102607727050781 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Wed Apr 23 11:22:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3609 begin to check gpu status inside check gpu memory 2025-04-23 11:22:13.376333: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2025-04-23 11:22:13.377121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:22:13.377217: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:22:13.377266: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:22:13.379646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:22:13.379724: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:22:13.382828: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:22:13.384172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:22:13.389974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:22:13.397540: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:22:13.398613: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2025-04-23 11:22:13.431178: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 3493065000 Hz 2025-04-23 11:22:13.432835: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7f5764000b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:22:13.432877: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version 2025-04-23 11:22:13.435883: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x45961f30 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: 2025-04-23 11:22:13.435904: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 2080 Ti, Compute Capability 7.5 2025-04-23 11:22:13.436840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:41:00.0 name: NVIDIA GeForce RTX 2080 Ti computeCapability: 7.5 coreClock: 1.545GHz coreCount: 68 deviceMemorySize: 10.76GiB deviceMemoryBandwidth: 573.69GiB/s 2025-04-23 11:22:13.437062: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:22:13.437086: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2025-04-23 11:22:13.437218: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2025-04-23 11:22:13.437246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2025-04-23 11:22:13.437279: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2025-04-23 11:22:13.437316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2025-04-23 11:22:13.437352: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2025-04-23 11:22:13.438315: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0 2025-04-23 11:22:13.438366: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2025-04-23 11:22:13.438414: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix: 2025-04-23 11:22:13.438424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0 2025-04-23 11:22:13.438434: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N 2025-04-23 11:22:13.439488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3096 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2080 Ti, pci bus id: 0000:41:00.0, compute capability: 7.5) l 3637 free memory gpu now : 6717 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3609 To do loadFromThcl(), then load ParamDescType : thcl3609 thcls : [{'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'}] thcl {'id': 3609, 'mtr_user_id': 31, 'name': 'tfhub_19_06_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'jrm,pcm,pcnc,pehd,tapis_vide', 'svm_portfolios_learning': '9336903,9336904,9336905,9336906,9336909', 'photo_hashtag_type': 4674, 'photo_desc_type': 5832, 'type_classification': 'tf_classification2', 'hashtag_id_list': '495916461,560181804,1284539308,628944319,2107748999'} Update svm_hashtag_type_desc : 5832 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5832, 'tfhub_19_06_2023', 1280, 1280, 'tfhub_19_06_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 6, 19, 12, 55, 22), datetime.datetime(2023, 6, 19, 12, 55, 22)) model_name : tfhub_19_06_2023 model_param file didn't exist model_name : tfhub_19_06_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) local folder : /data/models_weight/tfhub_19_06_2023 /data/models_weight/tfhub_19_06_2023/Confusion_Matrix.png size_local : 57753 size in s3 : 57753 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_jrm.jpg size_local : 79724 size in s3 : 79724 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcm.jpg size_local : 83556 size in s3 : 83556 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pcnc.jpg size_local : 74107 size in s3 : 74107 create time local : 2023-06-22 17:09:38 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_pehd.jpg size_local : 72705 size in s3 : 72705 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:20 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Precision_Recall_tapis_vide.jpg size_local : 70874 size in s3 : 70874 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:15 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 checkpoint already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216488 size in s3 : 216488 create time local : 2023-06-22 17:09:39 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279708 size in s3 : 32279708 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:21 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:22 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_19_06_2023/model_weights.h5 size_local : 16499144 size in s3 : 16499144 create time local : 2023-06-22 17:09:40 create time in s3 : 2023-06-19 10:55:15 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= module (KerasLayer) (None, 1280) 4049564 _________________________________________________________________ tfhub_19_06_2023dense (Dense (None, 5) 6405 ================================================================= Total params: 4,055,969 Trainable params: 6,405 Non-trainable params: 4,049,564 _________________________________________________________________ Loading Weights... time used to create the model : 11.986142873764038 time used to load_weights : 0.22968673706054688 0it [00:00, ?it/s] 3it [00:00, 747.83it/s]2025-04-23 11:22:28.315649: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 temp/1745400129_1620143_1171252764_29d5179a892cc50aadc9d67245534b59.jpg temp/1745400129_1620143_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg temp/1745400129_1620143_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg Found 3 images belonging to 1 classes. begin to do the prediction : time used to do the prediction : 3.296917676925659 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3609 time to traite the descriptors : 0.02917313575744629 storage_type for insertDescriptorsMulti : 3 To insert : 1171252764 To insert : 1171252487 To insert : 1171252784 time to insert the descriptors : 0.8813097476959229 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171252764, 1171252487, 1171252784] Looping around the photos to save general results len do output : 3 /1171252764Didn't retrieve data . /1171252487Didn't retrieve data . /1171252784Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252764', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252487', None, None, None, None, None, None) ('4567', None, None, None, None, None, None, None, None) ('4567', None, '1171252784', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.015359163284301758 save_final save missing photos in datou_result : time spend for datou_step_exec : 22.547212839126587 time spend to save output : 0.015794992446899414 total time spend for step 1 : 22.563007831573486 step2:argmax Wed Apr 23 11:22:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3609 Inside saveOutput : final : True verbose : False photo_id : 1171252764 output[photo_id] : [(1171252764, 'jrm', 0.9853728, 4674, '3609'), 'temp/1745400129_1620143_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'] photo_id : 1171252487 output[photo_id] : [(1171252487, 'jrm', 0.92636114, 4674, '3609'), 'temp/1745400129_1620143_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'] photo_id : 1171252784 output[photo_id] : [(1171252784, 'jrm', 0.9677402, 4674, '3609'), 'temp/1745400129_1620143_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.009794473648071289 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.01552438735961914 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.012570619583129883 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 5.245208740234375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.00023627281188964844 time spend to save output : 0.04258561134338379 total time spend for step 2 : 0.04282188415527344 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171252764': [(1171252764, 'jrm', 0.9853728, 4674, '3609'), 'temp/1745400129_1620143_1171252764_29d5179a892cc50aadc9d67245534b59.jpg'], '1171252487': [(1171252487, 'jrm', 0.92636114, 4674, '3609'), 'temp/1745400129_1620143_1171252487_5ebdd6b0a6bb39942a3808ed114806de.jpg'], '1171252784': [(1171252784, 'jrm', 0.9677402, 4674, '3609'), 'temp/1745400129_1620143_1171252784_5a3c5d3bb155a7a116f67ded51bffb59.jpg']} --------------------- test with use_multi_inputs=0 is succeded ------------------- ######################## test with use_multi_inputs=1 ######################## Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 12927 tfhub_classification2 is not linked in the step_by_step architecture ! WARNING : step 12928 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : tfhub_classification2, argmax list_input_json : [] origin BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.36322498321533203 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:tfhub_classification2 Wed Apr 23 11:22:32 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step TFHub with tf2 ! we are using the classfication for only one thcl 3655 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 5 1 Physical GPUs, 1 Logical GPUs tagging for thcl : 3655 To do loadFromThcl(), then load ParamDescType : thcl3655 thcls : [{'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'}] thcl {'id': 3655, 'mtr_user_id': 31, 'name': 'tfhub_18_7_2023', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'pcm,pcnc,jrm,pehd,tapis_vide', 'svm_portfolios_learning': '9336904,9336905,9336903,9336906,9336909', 'photo_hashtag_type': 4723, 'photo_desc_type': 5862, 'type_classification': 'tf_classification2', 'hashtag_id_list': '560181804,1284539308,495916461,628944319,2107748999'} Update svm_hashtag_type_desc : 5862 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5862, 'tfhub_18_7_2023', 1280, 1280, 'tfhub_18_7_2023', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2023, 7, 18, 22, 46, 29), datetime.datetime(2023, 7, 18, 22, 46, 29)) model_name : tfhub_18_7_2023 model_param file didn't exist model_name : tfhub_18_7_2023 model_type : tf_classification2 list file need : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file exist in s3 : ['Confusion_Matrix.png', 'Precision_Recall_jrm.jpg', 'Precision_Recall_pcm.jpg', 'Precision_Recall_pcnc.jpg', 'Precision_Recall_pehd.jpg', 'Precision_Recall_tapis_vide.jpg', 'Result_Summary.txt', 'checkpoint', 'model_checkpoint.ckpt.data-00000-of-00002', 'model_checkpoint.ckpt.data-00001-of-00002', 'model_checkpoint.ckpt.index', 'model_weights.h5'] file manque in s3 : [] local folder : /data/models_weight/tfhub_18_7_2023 /data/models_weight/tfhub_18_7_2023/Confusion_Matrix.png size_local : 54360 size in s3 : 54360 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:28 Confusion_Matrix.png already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_jrm.jpg size_local : 72583 size in s3 : 72583 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_jrm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcm.jpg size_local : 81681 size in s3 : 81681 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_pcm.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pcnc.jpg size_local : 79510 size in s3 : 79510 create time local : 2023-08-11 11:22:56 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pcnc.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_pehd.jpg size_local : 59936 size in s3 : 59936 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Precision_Recall_pehd.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Precision_Recall_tapis_vide.jpg size_local : 78974 size in s3 : 78974 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 Precision_Recall_tapis_vide.jpg already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/Result_Summary.txt size_local : 642 size in s3 : 642 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 Result_Summary.txt already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/checkpoint size_local : 99 size in s3 : 99 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:23 checkpoint already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00000-of-00002 size_local : 216529 size in s3 : 216529 create time local : 2023-08-11 11:22:57 create time in s3 : 2023-07-18 20:46:17 model_checkpoint.ckpt.data-00000-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.data-00001-of-00002 size_local : 32279748 size in s3 : 32279748 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.data-00001-of-00002 already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_checkpoint.ckpt.index size_local : 43546 size in s3 : 43546 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:19 model_checkpoint.ckpt.index already exist and didn't need to update /data/models_weight/tfhub_18_7_2023/model_weights.h5 size_local : 16500868 size in s3 : 16500868 create time local : 2023-08-11 11:22:58 create time in s3 : 2023-07-18 20:46:18 model_weights.h5 already exist and didn't need to update desc size : 1280 Model: "model" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 224, 224, 3) 0 __________________________________________________________________________________________________ input_2 (InputLayer) [(None, 1)] 0 __________________________________________________________________________________________________ module (KerasLayer) (None, 1280) 4049564 input_1[0][0] __________________________________________________________________________________________________ concatenate (Concatenate) (None, 1281) 0 input_2[0][0] module[0][0] __________________________________________________________________________________________________ tfhub_18_7_2023dense (Dense) (None, 5) 6410 concatenate[0][0] ================================================================================================== Total params: 4,055,974 Trainable params: 0 Non-trainable params: 4,055,974 __________________________________________________________________________________________________ Loading Weights... time used to create the model : 10.517524719238281 time used to load_weights : 0.21247577667236328 found 3 data found 0 labels begin to do the prediction : time used to do the prediction : 1.2850499153137207 (3,) (3, 5) (3, 1280) shape of features : (3, 1280) shape of new features : (1, 3, 1280) save descriptor for thcl : 3655 time to traite the descriptors : 0.044438838958740234 storage_type for insertDescriptorsMulti : 3 To insert : 1171291875 To insert : 1171275314 To insert : 1171275372 time to insert the descriptors : 1.5484116077423096 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tfhub_classification2 we use saveGeneral [1171291875, 1171275314, 1171275372] Looping around the photos to save general results len do output : 3 /1171291875Didn't retrieve data . /1171275314Didn't retrieve data . /1171275372Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171291875', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275314', None, None, None, None, None, None) ('4621', None, None, None, None, None, None, None, None) ('4621', None, '1171275372', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.013843297958374023 save_final save missing photos in datou_result : time spend for datou_step_exec : 16.86670231819153 time spend to save output : 0.014302492141723633 total time spend for step 1 : 16.881004810333252 step2:argmax Wed Apr 23 11:22:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 3655 Inside saveOutput : final : True verbose : False photo_id : 1171291875 output[photo_id] : [(1171291875, 'tapis_vide', 0.9706649, 4723, '3655'), 'temp/1745400152_1620143_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'] photo_id : 1171275314 output[photo_id] : [(1171275314, 'tapis_vide', 0.96524954, 4723, '3655'), 'temp/1745400152_1620143_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'] photo_id : 1171275372 output[photo_id] : [(1171275372, 'tapis_vide', 0.9674389, 4723, '3655'), 'temp/1745400152_1620143_1171275372_76d81364ff7df843bff095f45c07ba35.jpg'] begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.009597063064575195 begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 3 time used for this insertion : 0.012866497039794922 len list_finale : 3, len picture : 3 begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.01294851303100586 saving photo_ids in datou_result photo id not in port photo id not in port photo id not in port begin to insert list_values into mtr_datou_result : length of list_values in save_final : 0 time used for this insertion : 6.67572021484375e-06 save missing photos in datou_result : time spend for datou_step_exec : 0.0002205371856689453 time spend to save output : 0.040450334548950195 total time spend for step 2 : 0.04067087173461914 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'1171291875': [(1171291875, 'tapis_vide', 0.9706649, 4723, '3655'), 'temp/1745400152_1620143_1171291875_b62cd9e0d976b143f86fe82d072798c0.jpg'], '1171275314': [(1171275314, 'tapis_vide', 0.96524954, 4723, '3655'), 'temp/1745400152_1620143_1171275314_6e0a72c8fa00d5e4b018bd689b547133.jpg'], '1171275372': [(1171275372, 'tapis_vide', 0.9674389, 4723, '3655'), 'temp/1745400152_1620143_1171275372_76d81364ff7df843bff095f45c07ba35.jpg']} --------------------- test with use_multi_inputs=1 is succeded ------------------- ############################### TEST ordonner ################################ To do loadFromThcl(), then load ParamDescType : thcl358 thcls : [{'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'}] thcl {'id': 358, 'mtr_user_id': 31, 'name': 'car_orientation_0111', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'FirstUploadExperveo_vignette__port_505674,CAR_EXTERIEUR_Roue__port_503398,FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486,FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485,CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465,CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198,CAR_EXTERIEUR_Face_avant_axe_droit__port_504451,CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235,FirstUploadExperveo_vin__port_505675,CAR_EXTERIEUR_cote_droite__port_504108,CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565,FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483,CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201,cartegrise_orientation__port_505064,CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217,CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531,CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218,CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214,CAR_EXTERIEUR_Angle_avant_droit__port_504087,FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484,CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563,CAR_EXTERIEUR_Angle_arriere_droit__port_504160,CAR_EXTERIEUR_arriere__port_504184,CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562,INTERIEUR_Compteur_kilometrique__port_503644,CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494,CAR_EXTERIEUR_Angle_arriere_gauche__port_504170,CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226,CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202,CAR_EXTERIEUR_moteur__port_503704,FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487,CAR_INTERIEUR_siege_arriere_class_1__port_506551,CAR_EXTERIEUR_avant__port_504146,CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215,CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225,CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564,FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482,CAR_INTERIEUR_coffre__port_503412,FirstUploadExperveo_rouetranche__port_505677,UploadPhotoImmatBest_class_1__port_505051,CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532,CAR_EXTERIEUR_angle_avant_gauche__port_504098,CAR_EXTERIEUR_face_avant_axe_gauche__port_504236,CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540,CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233,CAR_EXTERIEUR_roue_de_secour__port_503763,CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199,CAR_EXTERIEUR_cote_gauche__port_504017,CAR_INTERIEUR_avant_volant_class_1__port_506503,CAR_INTERIEUR_avant_volant_class_2__port_506504,CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234', 'svm_portfolios_learning': '505674,503398,506486,506485,504465,504198,504451,504235,505675,504108,506565,506483,504201,505064,504217,506531,504218,504214,504087,506484,506563,504160,504184,506562,503644,506494,504170,504226,504202,503704,506487,506551,504146,504215,504225,506564,506482,503412,505677,505051,506532,504098,504236,506540,504233,503763,504199,504017,506503,506504,504234', 'photo_hashtag_type': 337, 'photo_desc_type': 3392, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'} Update svm_hashtag_type_desc : 3392 ['FirstUploadExperveo_vignette__port_505674', 'CAR_EXTERIEUR_Roue__port_503398', 'FirstUploadExperveo_carrosseriegrosplan_VIndanslamoquette__port_506486', 'FirstUploadExperveo_carrosseriegrosplan_siegegrosplan__port_506485', 'CAR_EXTERIEUR_Cote_droit_axe_avant__port_504465', 'CAR_EXTERIEUR_Cote_gauche_axe_arriere__port_504198', 'CAR_EXTERIEUR_Face_avant_axe_droit__port_504451', 'CAR_EXTERIEUR_angle_avant_gauche_axe_avant__port_504235', 'FirstUploadExperveo_vin__port_505675', 'CAR_EXTERIEUR_cote_droite__port_504108', 'CAR_INTERIEUR_avant_volant_class_6_levierdevitesse__port_506565', 'FirstUploadExperveo_carrosseriegrosplan_carrosserie__port_506483', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_arriere__port_504201', 'cartegrise_orientation__port_505064', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_arriere__port_504217', 'CAR_INTERIEUR_avant_vue-arriere_class_1__port_506531', 'CAR_EXTERIEUR_Face_arriere_axe_droit__port_504218', 'CAR_EXTERIEUR_Cote_droit_axe_arriere__port_504214', 'CAR_EXTERIEUR_Angle_avant_droit__port_504087', 'FirstUploadExperveo_carrosseriegrosplan_morceauderoue__port_506484', 'CAR_INTERIEUR_avant_volant_class_6_class_2__port_506563', 'CAR_EXTERIEUR_Angle_arriere_droit__port_504160', 'CAR_EXTERIEUR_arriere__port_504184', 'CAR_INTERIEUR_avant_volant_class_6_boutonrond__port_506562', 'INTERIEUR_Compteur_kilometrique__port_503644', 'CAR_INTERIEUR_avant_vue_gauche_habitacle_class_1__port_506494', 'CAR_EXTERIEUR_Angle_arriere_gauche__port_504170', 'CAR_EXTERIEUR_Angle_avant_droit_axe_arriere__port_504226', 'CAR_EXTERIEUR_Face_arriere_axe_gauche__port_504202', 'CAR_EXTERIEUR_moteur__port_503704', 'FirstUploadExperveo_carrosseriegrosplan_class_6__port_506487', 'CAR_INTERIEUR_siege_arriere_class_1__port_506551', 'CAR_EXTERIEUR_avant__port_504146', 'CAR_EXTERIEUR_Angle_arriere_droit_axe_droit__port_504215', 'CAR_EXTERIEUR_Angle_avant_droit_axe_droit__port_504225', 'CAR_INTERIEUR_avant_volant_class_6_ecrangrosplan__port_506564', 'FirstUploadExperveo_carrosseriegrosplan_moteurgrosplanetdegat__port_506482', 'CAR_INTERIEUR_coffre__port_503412', 'FirstUploadExperveo_rouetranche__port_505677', 'UploadPhotoImmatBest_class_1__port_505051', 'CAR_INTERIEUR_avant_vue-arriere_class_2__port_506532', 'CAR_EXTERIEUR_angle_avant_gauche__port_504098', 'CAR_EXTERIEUR_face_avant_axe_gauche__port_504236', 'CAR_INTERIEUR_avant_vue_droite_habitacle_class_1__port_506540', 'CAR_EXTERIEUR_cote_gauche_axe_avant__port_504233', 'CAR_EXTERIEUR_roue_de_secour__port_503763', 'CAR_EXTERIEUR_Angle_arriere_gauche_axe_gauche__port_504199', 'CAR_EXTERIEUR_cote_gauche__port_504017', 'CAR_INTERIEUR_avant_volant_class_1__port_506503', 'CAR_INTERIEUR_avant_volant_class_2__port_506504', 'CAR_EXTERIEUR_angle_avant_gauche_axe_gauche__port_504234'] 51 51 thcl : 358 photo_hashtag_type : 337 ############################### TEST rotate ################################ test rotate only Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18305015563964844 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Wed Apr 23 11:22:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! We are in a linear step without datou_depend ! rotate photos of 90,180,270 degres batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! Needs to change image size ! Needs to change image size ! About to upload 3 photos upload in portfolio : 551782 init cache_photo without model_param we have 3 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400170_1620143 we have uploaded 3 photos in the portfolio 551782 time of upload the photos Elapsed time : 1.7336688041687012 Len new_chis : 3 Len list_new_chi_with_photo_id : 0 of type : 0 time spend for datou_step_exec : 1.97688627243042 time spend to save output : 6.651878356933594e-05 total time spend for step 1 : 1.9769527912139893 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 3 /1353001533Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001536Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001537Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('230', None, None, None, None, None, None, None, None) ('230', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.01710033416748047 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1353001533: ['917849322', 'temp/1745400169_1620143_917849322_2bd260e91e91df8378dde8bb8b8c454890.jpg', []], 1353001536: ['917849322', 'temp/1745400169_1620143_917849322_2bd260e91e91df8378dde8bb8b8c4548180.jpg', []], 1353001537: ['917849322', 'temp/1745400169_1620143_917849322_2bd260e91e91df8378dde8bb8b8c4548270.jpg', []]} test rotate only is a success ! test rotate conditionnel Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13700151443481445 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:thcl Wed Apr 23 11:22:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 500 time to import caffe and check if the image exist : 0.00026297569274902344 time to convert the images to numpy array : 0.9831142425537109 total time to convert the images to numpy array : 0.9837749004364014 list photo_ids error: [] list photo_ids correct : [917849322] number of photos to traite : 1 try to delete the photos incorrect in DB tagging for thcl : 500 To do loadFromThcl(), then load ParamDescType : thcl500 thcls : [{'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'}] thcl {'id': 500, 'mtr_user_id': 31, 'name': 'orientation_carte_grise_all_2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'carteGrisesVerticales__port_549774,cartegrise_90deg__port_550987,cartesGrisesEnvers__port_549765,portfolio_270deg__port_550988', 'svm_portfolios_learning': '549774,550987,549765,550988', 'photo_hashtag_type': 507, 'photo_desc_type': 3517, 'type_classification': 'caffe', 'hashtag_id_list': '0,0,0,0'} Update svm_hashtag_type_desc : 3517 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) To loadFromThcl() : net_3517 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (3517, 'orientation_carte_grise_all_2', 16384, 25088, 'orientation_carte_grise_all_2', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2018, 4, 18, 20, 4, 34), datetime.datetime(2018, 4, 18, 20, 4, 34)) None mean_file_type : mean_file_path : prototxt_file_path : model : orientation_carte_grise_all_2 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : orientation_carte_grise_all_2 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : [] local folder : /data/models_weight/orientation_carte_grise_all_2 /data/models_weight/orientation_carte_grise_all_2/caffemodel size_local : 537110520 size in s3 : 537110520 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:17 caffemodel already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_conv_normal.prototxt size_local : 4626 size in s3 : 4626 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_conv_normal.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy_fc.prototxt size_local : 1130 size in s3 : 1130 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy_fc.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt size_local : 5653 size in s3 : 5653 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 deploy.prototxt already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:31 mean.npy already exist and didn't need to update /data/models_weight/orientation_carte_grise_all_2/synset_words.txt size_local : 159 size in s3 : 159 create time local : 2021-08-09 05:29:00 create time in s3 : 2021-08-06 20:07:16 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/orientation_carte_grise_all_2/deploy.prototxt caffemodel_filename : /data/models_weight/orientation_carte_grise_all_2/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'pool5']) time used to do the prepocess of the images : 1.820915937423706 time used to do the prediction : 0.11345863342285156 save descriptor for thcl : 500 time to traite the descriptors : 0.0669717788696289 storage_type for insertDescriptorsMulti : 1 To insert : 917849322 time to insert the descriptors : 0.6865098476409912 time spend for datou_step_exec : 8.844034194946289 time spend to save output : 4.935264587402344e-05 total time spend for step 1 : 8.844083547592163 step2:argmax Wed Apr 23 11:23:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step Argmax ! calculate argmax for thcl : 500 time spend for datou_step_exec : 0.000194549560546875 time spend to save output : 3.123283386230469e-05 total time spend for step 2 : 0.0002257823944091797 step3:rotate Wed Apr 23 11:23:01 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! We are in a datou with depends ! angle_condi : {'carteGrisesVerticales__port_549774': 0, 'cartegrise_90deg__port_550987': 270, 'portfolio_270deg__port_550988': 90, 'cartesGrisesEnvers__port_549765': 180} rotate photos for hashtag carteGrisesVerticales__port_549774 of 0 degres 1 photos founded : [917849322] batch 1 Loaded 0 chid ids of type : 0 map_chi of length : 0 Needs to change image size ! About to upload 1 photos upload in portfolio : 551782 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400181_1620143 we have uploaded 1 photos in the portfolio 551782 time of upload the photos Elapsed time : 0.7854025363922119 Len new_chis : 1 Len list_new_chi_with_photo_id : 0 of type : 0 rotate photos for hashtag cartegrise_90deg__port_550987 of 270 degres 0 photos founded : [] rotate photos for hashtag portfolio_270deg__port_550988 of 90 degres 0 photos founded : [] rotate photos for hashtag cartesGrisesEnvers__port_549765 of 180 degres 0 photos founded : [] time spend for datou_step_exec : 0.8869566917419434 time spend to save output : 3.552436828613281e-05 total time spend for step 3 : 0.8869922161102295 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [917849322] Looping around the photos to save general results len do output : 1 /1353001662Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('233', None, None, None, None, None, None, None, None) ('233', None, '917849322', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.016454696655273438 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1353001662: ['917849322', 'temp/1745400172_1620143_917849322_2bd260e91e91df8378dde8bb8b8c45480.jpg', []]} ############################### TEST data_augmentation_ellipse_varroa_tile_rotate ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : DATA AUGMENTATION ELLIPSE VARROA TILE ROTATE Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 316 crop is not linked in the step_by_step architecture ! Step 318 rotate have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 318 rotate have less outputs used (0) than in the step definition (3) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : crop, tile, rotate list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.15738701820373535 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 3 step1:crop Wed Apr 23 11:23:02 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'hashtag_id_ellipse': 2087736828, 'photo_hashtag_type_from_ellipse': 520, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'crop_detect_varroa', 'photo_hashtag_type': 407, 'feed_id_new_photos_not_used': 549103, 'host': 'www.fotonower.com', 'margin': 8, 'upload_type': 'python'} margin_type : margin margin_value : [8, 8, 8, 8] Loading chi in step crop with photo_hashtag_type : 407 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 4 chid ids of type : 407 +WARNING : Unexpected points, we should remove this data for chi_id : 8165075, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165076, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165077, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 8165078, for now we just ignore these empty polygon points WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 4 Here we crop with rles About to insert : list_path_to_insert length 4 new photo from crops ! About to upload 4 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=crop_detect_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22341511 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400184_1620143 we have uploaded 4 photos in the portfolio 22341511 time of upload the photos Elapsed time : 3.450540065765381 Now we prepare data that will be used for ellipse search ! About to compute ellipse and record with type : 520 score : 5120 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2311 | arg_min : -30.0 min_score : 1968 | arg_min : 17.8125 min_score : 1614 | arg_min : 31.875 min_score : 1105 | arg_min : 28.5 min_score : 1105 arg_min : 1.9500000000000002 min_score : 1105 arg_min : 25.0 min_score : 1088 arg_min : 24.9375 min_score : 979 arg_min : 31.875 min_score : 979 arg_min : 28.5 min_score : 979 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 yc : 31.875 xc : 24.9375 angle : 25.0 radius : 28.5 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 1 chid ids of type : 520 CHI and polygons saved ! score : 5362 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 2281 | arg_min : -10.0 min_score : 2127 | arg_min : 25.0 min_score : 2127 | arg_min : 30.9375 min_score : 714 | arg_min : 25.0 min_score : 714 arg_min : 1.9500000000000002 min_score : 714 arg_min : -5.0 min_score : 668 arg_min : 23.4375 min_score : 655 arg_min : 29.53125 min_score : 631 arg_min : 25.0 min_score : 631 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 yc : 29.53125 xc : 23.4375 angle : -5.0 radius : 25.0 excentricity : 1.9500000000000002 Now saving polygons points : 1| batch 1 Loaded 2 chid ids of type : 520 + CHI and polygons saved ! score : 4603 strategy_opt : 5| | arg_min : 1.85 min_score : 2981 | arg_min : -50.0 min_score : 1356 | arg_min : 30.28125 min_score : 1079 | arg_min : 23.625 min_score : 995 | arg_min : 27.0 min_score : 995 arg_min : 1.6500000000000001 min_score : 961 arg_min : -70.0 min_score : 852 arg_min : 28.6875 min_score : 847 arg_min : 23.625 min_score : 847 arg_min : 27.0 min_score : 847 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 yc : 23.625 xc : 28.6875 angle : -70.0 radius : 27.0 excentricity : 1.6500000000000001 Now saving polygons points : 1| batch 1 Loaded 3 chid ids of type : 520 ++ CHI and polygons saved ! score : 7970 strategy_opt : 5| | arg_min : 1.9500000000000002 min_score : 1576 | arg_min : 40.0 min_score : 632 | arg_min : 20.15625 min_score : 561 | arg_min : 26.0 min_score : 561 | arg_min : 26.0 min_score : 561 arg_min : 1.8 min_score : 520 arg_min : 40.0 min_score : 520 arg_min : 18.8125 min_score : 494 arg_min : 26.0 min_score : 494 arg_min : 26.0 min_score : 494 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 yc : 26.0 xc : 18.8125 angle : 40.0 radius : 26.0 excentricity : 1.8 Now saving polygons points : 1| batch 1 Loaded 4 chid ids of type : 520 +++ CHI and polygons saved ! ['temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165075_0_varroa_with_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165076_0_varroa_with_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165077_0_varroa_with_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_ellipsebest.jpg', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_bib_crop_8165078_0_varroa_with_ellipsebest.jpg'] About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22341512 Result OK ! uploaded one batch 0 Elapsed time : 19.768162965774536 time spend for datou_step_exec : 26.857068061828613 time spend to save output : 2.0742416381835938e-05 total time spend for step 1 : 26.857088804244995 step2:tile Wed Apr 23 11:23:29 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'photo_tile_type': 17, 'whiten': True, 'remove_crop_border': True, 'minimal_size_crop_border': 900, 'stride': 240, 'crop_hashtag_type_tiled': 521, 'ETA': 86400, 'new_width': 480, 'new_height': 480, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_taggage_varroa', 'crop_hashtag_type': 520, 'host': 'www.fotonower.com', 'arg_aux_upload': {'type_upload': 'python'}} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {520: 521} TO DEPRECATE VR 14-6-18 map_filenames : {937852786: 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 4 chid ids of type : 520 ++++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_taggage_varroa&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22341513 with name tile_taggage_varroa feed_id_new_photos : 22341513 filename : temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg photo_id : 937852786 height_image_input : 480 width_image_input : 480 new_width : 480 new_height : 480 stride : 240 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 4 list_bib_to_crops : 1 [(0, 480, 0, 480, 0)] new_crops_tiles : 1 crop_transformed : 4 batch 1 Loaded 1 chid ids of type : 17 treat the image : temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67.jpg , 0 before upload mediasElapsed time : 0.010460853576660156 on upload les photos avec python init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400216_1620143 we have uploaded 1 photos in the portfolio 22341513 Importing ! upload mediasElapsed time : 0.7234396934509277 , 0Saving 4 CHIs. batch 1 Loaded 4 chid ids of type : 521 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 0.7970218658447266 time spend for datou_step_exec : 7.636595964431763 time spend to save output : 3.075599670410156e-05 total time spend for step 2 : 7.636626720428467 step3:rotate Wed Apr 23 11:23:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a datou with depends ! rotate photos of 0,15,30,45,60,75,90,105,120,135,150,165,180,195,210,225,240,255,270,285,300,315,330,345 degres batch 1 Loaded 4 chid ids of type : 521 ++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=rotate_data_augmentation_varroa_480_ellipse_320&access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 22341515 Needs to change image size ! time for calcul the mask position with numpy : 0.0005428791046142578 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004052400588989258 .time for calcul the mask position with numpy : 0.0004527568817138672 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0033118724822998047 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003745555877685547 nb_pixel_total : 694 time to create 1 rle with old method : 0.0022220611572265625 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003669261932373047 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0035238265991210938 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003688335418701172 nb_pixel_total : 221 time to create 1 rle with old method : 0.0007290840148925781 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036406517028808594 nb_pixel_total : 1155 time to create 1 rle with old method : 0.0035316944122314453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003688335418701172 nb_pixel_total : 143 time to create 1 rle with old method : 0.0005054473876953125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00038170814514160156 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0035483837127685547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003764629364013672 nb_pixel_total : 414 time to create 1 rle with old method : 0.001058816909790039 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003783702850341797 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0035746097564697266 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0004246234893798828 nb_pixel_total : 1204 time to create 1 rle with old method : 0.003564119338989258 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003879070281982422 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003969430923461914 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003960132598876953 nb_pixel_total : 264 time to create 1 rle with old method : 0.0009679794311523438 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040411949157714844 nb_pixel_total : 1389 time to create 1 rle with old method : 0.0040934085845947266 .time for calcul the mask position with numpy : 0.0004010200500488281 nb_pixel_total : 1157 time to create 1 rle with old method : 0.0035026073455810547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003864765167236328 nb_pixel_total : 694 time to create 1 rle with old method : 0.0017521381378173828 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003631114959716797 nb_pixel_total : 1162 time to create 1 rle with old method : 0.0028007030487060547 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003809928894042969 nb_pixel_total : 221 time to create 1 rle with old method : 0.0006453990936279297 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003726482391357422 nb_pixel_total : 1155 time to create 1 rle with old method : 0.002849578857421875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003707408905029297 nb_pixel_total : 143 time to create 1 rle with old method : 0.0004649162292480469 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003674030303955078 nb_pixel_total : 1160 time to create 1 rle with old method : 0.002874135971069336 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003833770751953125 nb_pixel_total : 414 time to create 1 rle with old method : 0.0014789104461669922 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0004303455352783203 nb_pixel_total : 1159 time to create 1 rle with old method : 0.003340005874633789 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00040268898010253906 nb_pixel_total : 1 time to create 1 rle with old method : 3.0517578125e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.000423431396484375 nb_pixel_total : 1204 time to create 1 rle with old method : 0.0035996437072753906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037384033203125 nb_pixel_total : 1158 time to create 1 rle with old method : 0.003551959991455078 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00033020973205566406 nb_pixel_total : 264 time to create 1 rle with old method : 0.0008356571197509766 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00042748451232910156 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004056453704833984 .time for calcul the mask position with numpy : 0.00036215782165527344 nb_pixel_total : 1157 time to create 1 rle with old method : 0.002802133560180664 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00047659873962402344 nb_pixel_total : 727 time to create 1 rle with old method : 0.002277374267578125 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003337860107421875 nb_pixel_total : 1162 time to create 1 rle with old method : 0.002758026123046875 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039577484130859375 nb_pixel_total : 250 time to create 1 rle with old method : 0.0007293224334716797 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003502368927001953 nb_pixel_total : 1155 time to create 1 rle with old method : 0.003513336181640625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040459632873535156 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005316734313964844 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003886222839355469 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0035047531127929688 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00040149688720703125 nb_pixel_total : 450 time to create 1 rle with old method : 0.0015397071838378906 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00037980079650878906 nb_pixel_total : 1159 time to create 1 rle with old method : 0.0031197071075439453 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.00034928321838378906 nb_pixel_total : 1 time to create 1 rle with old method : 2.6464462280273438e-05 Needs to change image size ! time for calcul the mask position with numpy : 0.000408172607421875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.0030438899993896484 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003802776336669922 nb_pixel_total : 1158 time to create 1 rle with old method : 0.003500223159790039 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003654956817626953 nb_pixel_total : 234 time to create 1 rle with old method : 0.0007002353668212891 On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.00045800209045410156 nb_pixel_total : 1389 time to create 1 rle with old method : 0.004075527191162109 .time for calcul the mask position with numpy : 0.0003879070281982422 nb_pixel_total : 1157 time to create 1 rle with old method : 0.003495454788208008 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00039076805114746094 nb_pixel_total : 727 time to create 1 rle with old method : 0.002420186996459961 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003514289855957031 nb_pixel_total : 1162 time to create 1 rle with old method : 0.003082275390625 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037860870361328125 nb_pixel_total : 250 time to create 1 rle with old method : 0.0006964206695556641 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00035500526428222656 nb_pixel_total : 1155 time to create 1 rle with old method : 0.003511667251586914 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003650188446044922 nb_pixel_total : 169 time to create 1 rle with old method : 0.0005714893341064453 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003581047058105469 nb_pixel_total : 1161 time to create 1 rle with old method : 0.0035448074340820312 . crop are not in the shrunk photo ! crop are not in the shrunk photo ! Needs to change image size ! time for calcul the mask position with numpy : 0.00037360191345214844 nb_pixel_total : 450 time to create 1 rle with old method : 0.0015454292297363281 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.0003573894500732422 nb_pixel_total : 1159 time to create 1 rle with old method : 0.003511667251586914 . crop are not in the shrunk photo ! On the border Smaller than minimal size ! Needs to change image size ! time for calcul the mask position with numpy : 0.0003948211669921875 nb_pixel_total : 1237 time to create 1 rle with old method : 0.003663301467895508 On the border Smaller than minimal size ! time for calcul the mask position with numpy : 0.00036525726318359375 nb_pixel_total : 1157 time to create 1 rle with old method : 0.02013707160949707 . crop are not in the shrunk photo ! time for calcul the mask position with numpy : 0.0003807544708251953 nb_pixel_total : 234 time to create 1 rle with old method : 0.0008308887481689453 On the border Smaller than minimal size ! About to upload 24 photos upload in portfolio : 22341515 init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400219_1620143 we have uploaded 24 photos in the portfolio 22341515 time of upload the photos Elapsed time : 6.983797788619995 Len new_chis : 24 Len list_new_chi_with_photo_id : 28 of type : 529 batch 1 Loaded 28 chid ids of type : 529 Number RLEs to save : 1197 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 28 chid ids of type : 529 ++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 10.765246868133545 time spend to save output : 8.7738037109375e-05 total time spend for step 3 : 10.765334606170654 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : rotate we use saveGeneral [937852786, 937852786, '1353001902'] Looping around the photos to save general results len do output : 24 /1353001962Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001963Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001964Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001966Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001968Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001969Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001971Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001972Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001973Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001975Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001976Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001977Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001979Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001980Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001981Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001983Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001984Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001985Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001989Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001990Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001991Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001993Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001994Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353001995Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '937852786', None, None, None, None, None, None) ('243', None, None, None, None, None, None, None, None) ('243', None, '1353001902', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 75 time used for this insertion : 0.019272327423095703 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 3 output : {1353001962: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_00.jpg', [, ]], 1353001963: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_015.jpg', []], 1353001964: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_030.jpg', []], 1353001966: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_045.jpg', []], 1353001968: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_060.jpg', []], 1353001969: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_075.jpg', []], 1353001971: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_090.jpg', [, ]], 1353001972: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0105.jpg', []], 1353001973: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0120.jpg', []], 1353001975: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0135.jpg', []], 1353001976: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0150.jpg', []], 1353001977: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0165.jpg', []], 1353001979: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0180.jpg', [, ]], 1353001980: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0195.jpg', []], 1353001981: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0210.jpg', []], 1353001983: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0225.jpg', []], 1353001984: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0240.jpg', []], 1353001985: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0255.jpg', []], 1353001989: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0270.jpg', [, ]], 1353001990: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0285.jpg', []], 1353001991: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0300.jpg', []], 1353001993: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0315.jpg', []], 1353001994: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0330.jpg', []], 1353001995: ['937852786', 'temp/1745400182_1620143_937852786_7d9a231a08a1c63d0868e56a5361bf67_0345.jpg', []]} list chi : [[, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], [], [, ], [], [], [], [], []] ############################### TEST flip ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : flip list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.12444758415222168 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:flip Wed Apr 23 11:23:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_flip ! We are in a linear step without datou_depend ! batch 1 Loaded 6 chid ids of type : 741 +++++WARNING : Unexpected points, we should remove this data for chi_id : 18344210, for now we just ignore these empty polygon points + map_chi_objs of length : 1 photo_id in download_rotate_and_save : 911785586 list_chi_loc : 6 Vertical flip of photo 911785586 Horizontal flip of photo 911785586 About to upload 2 photos upload in portfolio : 1090565 init cache_photo without model_param we have 2 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400228_1620143 we have uploaded 2 photos in the portfolio 1090565 time of upload the photos Elapsed time : 0.8471701145172119 Len new_chis : 12 Len list_new_chi_with_photo_id : 12 of type : 741 batch 1 Loaded 12 chid ids of type : 741 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.9678330421447754 time spend to save output : 0.0001163482666015625 total time spend for step 1 : 0.967949390411377 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : flip we use saveGeneral [911785586] Looping around the photos to save general results len do output : 2 /1353002016 /1353002017 before output type Managing all output in save final without adding information in the mtr_datou_result ('571', None, None, None, None, None, None, None, None) ('571', None, '911785586', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 time used for this insertion : 0.01586461067199707 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1353002016': ['911785586', 'temp/1745400227_1620143_911785586_d8582feabcd359151ff718b5832248c7-big_flip_vert.jpg', [, , , , , ]], '1353002017': ['911785586', 'temp/1745400227_1620143_911785586_d8582feabcd359151ff718b5832248c7-big_flip_hori.jpg', [, , , , , ]]} ############################### TEST crop_rles ################################ # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! Unexpected type seems boolean for variable list_input_json ERROR or WARNING : can't parse json string Expecting value: line 1 column 1 (char 0) Tried to parse : TEST CROP RLES Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.16323089599609375 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop Wed Apr 23 11:23:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Crop ! param_json : {'photo_hashtag_type': 755, 'token': '78d09a0790ec6ecbf119343125a81fdc', 'feed_id_new_photos': 0, 'host': 'www.fotonower.com', 'crop_type': 'rle', 'margin_relative': 0.1, 'min_score': 0.3, 'upload,type': 'python'} margin_type : margin_relative margin_value : [0.1, 0.1, 0.1, 0.1] Loading chi in step crop with photo_hashtag_type : 755 Loading chi in step crop for list_pids : 1 ! batch 1 Loaded 8 chid ids of type : 755 ++++++++WARNING : margin is only used for type bib ! we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles we have both polygon and rles map_result returned by crop_photo_return_map_crop : length : 8 Here we crop with rles About to insert : list_path_to_insert length 8 new photo from crops ! About to upload 8 photos https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc upload in portfolio : 22341540 Result OK ! uploaded one batch 0 Elapsed time : 20.279388189315796 Now we prepare data that will be used for ellipse search ! time spend for datou_step_exec : 20.34010672569275 time spend to save output : 3.218650817871094e-05 total time spend for step 1 : 20.340138912200928 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop we use saveGeneral [950103132] Looping around the photos to save general results len do output : 8 /1353002044Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002058Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002072Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002086Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002098Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002123Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002208Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353002230Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('686', None, None, None, None, None, None, None, None) ('686', None, '950103132', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 25 time used for this insertion : 0.01747441291809082 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1353002044': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670931_0.jpg', (183, 199, 15, 41)], '1353002058': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670932_0.jpg', (38, 85, 113, 140)], '1353002072': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670933_0.jpg', (168, 194, 141, 151)], '1353002086': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670934_0.jpg', (47, 101, 16, 110)], '1353002098': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670935_0.jpg', (175, 199, 104, 111)], '1353002123': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670936_0.jpg', (86, 130, 184, 196)], '1353002208': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670937_0.jpg', (79, 195, 0, 61)], '1353002230': ['950103132', 'temp/1745400228_1620143_950103132_4f47bd527301396b0a701a1b4183ba00_rle_crop_1947670938_0.jpg', (131, 155, 181, 195)]} 8 ERROR TEST CROP RLES different hash of same crop : hash_non_reg : f4e274dd08c90f327e74f1b91ebc5e43761642ac4456156033042a4b while hash_to_check : 741e1753b71b2b6b2879a507a69a00f8933bca84317a40e04a011d77 ERROR crop_rles FAILED ############################### TEST angular_coeff ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : angular_coeff list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.21331071853637695 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:angular_coeff Wed Apr 23 11:24:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 846, 'output_type': -1, 'orientation_type': 872, 'ref_crop_type': 846, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'crops_coeffs': {'CAR_EXTERIEUR_angle_avant_droit.*': {'aile-avant': [[15, 0.0], [240, 0.0], [285, 1.0], [345, 1.0]], 'capot': [[45, 1.0], [60, 0.5], [270, 0.0], [315, 1.0], [360, 1.0]]}}} angular_coefficients_to_crops batch 1 Loaded 19 chid ids of type : 846 treating photo 932296368 time spend for datou_step_exec : 0.0564420223236084 time spend to save output : 3.314018249511719e-05 total time spend for step 1 : 0.056475162506103516 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {932296368: ([(932296368, 2106233860, 846, 1066, 1277, 93, 340, 0.31964028378983567, 0, []), (932296368, 2106233860, 846, 434, 690, 218, 498, 0.7170410105787726, 0, []), (932296368, 503548896, 846, 902, 1111, 466, 576, 0.31724966, 769189715, []), (932296368, 599722655, 846, 523, 1100, 152, 337, 0.98039776, 0, []), (932296368, 492601069, 846, 143, 1190, 90, 695, 0.9696157, 769189717, []), (932296368, 492601069, 846, 0, 408, 246, 719, 0.9431181, 769189718, []), (932296368, 2096875722, 846, 567, 964, 162, 215, 0.55490255, 769189721, []), (932296368, 2096875709, 846, 437, 939, 24, 198, 0.9983077, 769189723, []), (932296368, 2096875709, 846, 1004, 1263, 28, 144, 0.9485744, 769189724, []), (932296368, 624624117, 846, 595, 1122, 331, 640, 0.99100167, 769189725, []), (932296368, 492624020, 846, 585, 874, 308, 393, 0.78697366, 769189727, []), (932296368, 2096875719, 846, 943, 1100, 428, 547, 0.96733797, 769189729, []), (932296368, 492654799, 846, 253, 467, 35, 441, 0.99621326, 769189730, []), (932296368, 492689227, 846, 1118, 1264, 270, 438, 0.9901647, 769189732, []), (932296368, 492689227, 846, 486, 671, 378, 690, 0.98789483, 769189733, []), (932296368, 492689227, 846, 161, 255, 229, 409, 0.70801014, 769189734, []), (932296368, 492925064, 846, 261, 421, 27, 193, 0.92215157, 769189737, []), (932296368, 492925064, 846, 873, 1045, 46, 156, 0.7535122, 769189738, []), (932296368, 492925064, 846, 1090, 1279, 20, 107, 0.45259848, 769189739, [])],)} test angular coeff is a success ! ############################### TEST detection_filter_by_crop ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_crop list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.168715238571167 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_crop Wed Apr 23 11:24:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter param_json : {'input_type': 631, 'output_type': -1, 'condition_type': 445, 'condition_crop': 'car', 'criteria_crop': 'center_rect', 'min_surface_ratio': 0.7} conditional_crop_copy batch 1 Loaded 3 chid ids of type : 445 +++batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++batch 1 Loaded 3 chid ids of type : 445 +++ treating photo 946711423 time spend for datou_step_exec : 0.12559914588928223 time spend to save output : 9.5367431640625e-05 total time spend for step 1 : 0.12569451332092285 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {946711423: ([(946711423, 624624117, 631, 226, 569, 252, 425, 0.99812776, 1947740368, ['395,419,341,419,340,418,316,418,315,417,306,417,305,416,293,415,290,413,284,412,283,411,280,411,272,407,264,405,258,400,254,398,250,394,244,391,242,389,242,386,239,380,240,368,239,367,239,347,238,346,238,331,237,330,237,327,238,326,237,314,239,311,239,308,237,304,238,302,243,298,244,296,244,292,246,291,250,291,251,290,259,290,260,289,264,289,265,288,269,288,271,290,273,294,278,299,280,300,285,300,286,301,293,301,294,302,302,304,305,307,309,308,312,310,314,310,317,312,335,312,336,313,343,313,344,314,370,314,371,315,381,315,382,314,389,313,393,311,405,309,406,308,408,308,412,306,414,304,417,304,421,307,426,308,427,309,433,309,434,310,464,309,467,306,471,304,476,304,477,303,489,303,490,302,494,302,495,301,500,301,501,300,515,300,516,299,519,298,522,292,525,290,533,290,534,291,540,291,541,290,543,290,547,288,550,285,550,285,552,289,552,291,553,292,553,313,552,314,552,324,550,328,550,333,549,334,549,336,544,346,543,353,539,361,532,368,531,368,527,372,519,374,509,379,503,384,499,385,498,386,496,386,492,388,490,390,486,392,484,392,479,396,475,397,474,398,472,398,471,399,469,399,462,403,460,403,459,404,457,404,456,405,454,405,450,407,448,407,443,410,425,413,424,414,422,414,416,417,404,417,403,418,396,418']), (946711423, 492689227, 631, 162, 245, 233, 396, 0.99702626, 1947740369, ['215,393,206,393,202,390,200,390,192,383,191,380,187,375,184,369,184,367,180,360,180,358,179,357,177,349,175,347,174,339,172,336,171,330,170,329,169,324,168,323,168,313,167,312,167,304,166,303,166,298,165,297,165,288,164,287,165,286,165,272,166,271,166,268,167,267,167,263,168,262,169,254,173,249,177,247,178,247,181,251,184,251,184,252,187,255,189,255,193,259,193,261,195,263,195,264,201,270,203,278,207,282,208,289,211,293,211,296,213,299,214,304,215,305,216,312,219,316,219,319,220,320,220,325,222,329,222,335,223,336,223,338,225,342,225,349,226,350,226,359,227,360,227,366,228,367,228,371,231,375,231,382,227,385,226,388,225,389,223,388,219,392,216,392']), (946711423, 492654799, 631, 96, 172, 39, 261, 0.9928518, 1947740370, ['143,252,143,249,141,246,140,246,138,248,138,251,137,250,137,248,135,246,134,246,132,248,127,244,124,244,122,241,122,236,121,235,121,232,118,229,117,225,116,224,116,212,113,209,115,207,116,201,111,194,110,184,106,178,107,154,108,152,112,148,113,144,112,143,112,138,110,136,108,136,107,135,103,128,103,124,102,123,102,121,103,120,103,118,106,115,106,106,107,105,110,104,113,101,117,93,117,71,114,65,116,61,116,59,117,58,117,55,118,54,119,49,122,45,122,44,124,42,150,42,151,43,153,43,153,47,152,48,152,50,154,52,155,56,156,57,156,85,155,86,155,95,154,96,154,98,155,99,155,105,156,106,155,107,155,116,157,120,159,121,159,123,156,127,156,134,157,135,157,138,156,139,156,141,154,145,152,147,150,151,149,159,148,160,148,164,149,165,149,174,148,175,148,197,149,198,149,215,150,216,150,241,149,242,149,245,148,247,146,245,144,247', '122,147,121,138,120,141,119,142,119,144,118,145,121,148']), (946711423, 2096875719, 631, 468, 555, 292, 365, 0.9830025, 1947740372, 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'519,353,518,352,517,353,518,354'])],)} test detection filter by crop is a success ! ############################### TEST detection_filter_by_classif ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detection_filter_by_classif list_input_json : [] origin we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.004019260406494141 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detection_filter_by_classif Wed Apr 23 11:24:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of step detection filter with classification results param_json : {'input_type': 631, 'output_type': 816, 'condition_type': 872, 'crops_ok': {'CAR_DOCUMENT.*': {}, 'CAR_INTERIEUR.*': {}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'Retroviseur': 2, 'Roue': 2, 'Capot': 1, 'Pare-brise': 1, 'vitre': 10, 'phare': 2, 'Feu-antibrouillard': 2, 'poignee': 2, 'porte': 2, 'calandre': 1, 'logo-marque': 1, 'Plaque-immatriculation': 1, 'Essuie-glace': 1, 'pare-choc': 1, 'toit': 1, 'logo-roue': 1, 'aile-avant': 1}}, 'separation': {'CAR_EXTERIEUR_avant.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-gauche', 'a-droite-de', 'phare-droit']}, 'CAR_EXTERIEUR_angle_avant_droit.*': {'pare-choc': ['pare-chocs-avant'], 'phare': ['phare-droite', 'a-gauche-de', 'phare-gauche'], 'porte': ['porte-avant', 'a-droite-de', 'porte-arriere']}}} conditional_crop_by_classif_copy batch 1 Loaded 35 chid ids of type : 631 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ treating photo 946711423 batch 1 Loaded 0 chid ids of type : 0 batch 1 Loaded 23 chid ids of type : 816 Number RLEs to save : 1600 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.3216245174407959 time spend to save output : 9.202957153320312e-05 total time spend for step 1 : 0.3217165470123291 caffe_path_current : About to save ! 0 After save, about to update current ! test detection filter by classif is a success ! ############################### TEST blur_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : blur_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.13446283340454102 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:blur_detection Wed Apr 23 11:24:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blur_detection methode: ratio et variance treat image : temp/1745400250_1620143_930729675_b2d2beaaee733d521cbb0c9800a29073.jpg resize: (600, 800) 930729675 12.961859636534896 time spend for datou_step_exec : 0.3062140941619873 time spend to save output : 5.316734313964844e-05 total time spend for step 1 : 0.30626726150512695 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {930729675: [(930729675, 12.961859636534896, 492688767)]} {930729675: [(930729675, 12.961859636534896, 492688767)]} ############################### TEST detect_point_224x224 ################################ test_detect_point_224x224 Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 4589 thcl is not linked in the step_by_step architecture ! WARNING : step 4590 argmax is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : thcl, argmax list_input_json : [] origin BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 1.6177165508270264 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:thcl Wed Apr 23 11:24:12 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Thcl ! we are using the classfication for only one thcl 1528 time to import caffe and check if the image exist : 0.0007967948913574219 time to convert the images to numpy array : 0.008980512619018555 time to import caffe and check if the image exist : 0.0033388137817382812 time to convert the images to numpy array : 0.044384002685546875 time to import caffe and check if the image exist : 0.01601386070251465 time to convert the images to numpy array : 0.04403257369995117 time to import caffe and check if the image exist : 0.007540464401245117 time to convert the images to numpy array : 0.052657365798950195 time to import caffe and check if the image exist : 0.008795738220214844 time to convert the images to numpy array : 0.049520015716552734 time to import caffe and check if the image exist : 0.010108709335327148 time to convert the images to numpy array : 0.05030179023742676 time to import caffe and check if the image exist : 0.013856649398803711 time to convert the images to numpy array : 0.048872947692871094 time to import caffe and check if the image exist : 0.0046808719635009766 time to convert the images to numpy array : 0.057042837142944336 time to import caffe and check if the image exist : 0.024086952209472656 time to convert the images to numpy array : 0.041841983795166016 time to import caffe and check if the image exist : 0.015292644500732422 time to convert the images to numpy array : 0.04997396469116211 total time to convert the images to numpy array : 0.06664586067199707 list photo_ids error: [] list photo_ids correct : [987515238, 987515211, 987515212, 987515213, 987515215, 987515216, 987515217, 987515219, 987515246, 987515247, 987515248, 987515249, 987515250, 987515175, 987515176, 987515177, 987515178, 987515179, 987515180, 987515181, 987515182, 987515183, 987515193, 987515195, 987515196, 987515198, 987515200, 987515201, 987515202, 987515204, 987515205, 987515224, 987515226, 987515227, 987515228, 987515230, 987515220, 987515222, 987515223, 987515188, 987515189, 987515190, 987515192, 987515231, 987515232, 987515233, 987515234, 987515235, 987515236, 987515237, 987515239, 987515240, 987515241, 987515242, 987515243, 987515244, 987515245, 987515184, 987515185, 987515186, 987515187, 987515207, 987515208, 987515209] number of photos to traite : 64 try to delete the photos incorrect in DB tagging for thcl : 1528 To do loadFromThcl(), then load ParamDescType : thcl1528 thcls : [{'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'}] thcl {'id': 1528, 'mtr_user_id': 31, 'name': 'learn_refus_upm_blanches_1924', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Autre_Environement,Carton,Kraft,Lointain_Papier_Magazine,Metal,Papier_Magazine,Plastique,Sol_Environement,Teint_Dans_La_Masse,autre_refus', 'svm_portfolios_learning': '0,0,0,0,0,0,0,0,0,0', 'photo_hashtag_type': 1927, 'photo_desc_type': 4421, 'type_classification': 'caffe', 'hashtag_id_list': '2107752388,492774966,493202403,2107752389,492628673,2107752386,492725882,2107752387,2107752385,2107752406'} Update svm_hashtag_type_desc : 4421 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) To loadFromThcl() : net_4421 begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (4421, 'learn_refus_upm_blanches_1924', 16384, 25088, 'learn_refus_upm_blanches_1924', 'res5b', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 1, datetime.datetime(2019, 10, 22, 17, 39, 25), datetime.datetime(2019, 10, 22, 17, 39, 25)) None mean_file_type : mean_file_path : prototxt_file_path : model : learn_refus_upm_blanches_1924 Inside get_net Inside get_net before cache_data_model model_param file didn't exist Inside get_net before CDM.load_model_par_type model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update Inside get_net after CDM.load_model_par_type After if not only_with_local_cache: /home/admin/workarea/install/darknet/:/home/admin/workarea/git/Velours/python:/home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python:/home/admin/mtr/.credentials:/home/admin/workarea/install/caffe/python:/home/admin/workarea/install/caffe_frcnn/py-faster-rcnn/tools/:/home/admin/workarea/git/fotonowerpip/:/home/admin/workarea/install/segment-anything:/home/admin//workarea/git/pyfvs/ Here before set mode gpu Doing nothing but we could set mode gpu after set mode gpu prototxt_filename : /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt caffemodel_filename : /data/models_weight/learn_refus_upm_blanches_1924/caffemodel now we set caffe to gpu mode before predict begin to check gpu status inside check gpu memory l 3637 free memory gpu now : 3165 max_wait_temp : 1 max_wait : 0 dict_keys(['prob', 'res5b']) time used to do the prepocess of the images : 0.057027339935302734 time used to do the prediction : 0.253603458404541 save descriptor for thcl : 1528 time to traite the descriptors : 4.247423410415649 storage_type for insertDescriptorsMulti : 1 To insert : 987515238 To insert : 987515211 To insert : 987515212 To insert : 987515213 To insert : 987515215 To insert : 987515216 To insert : 987515217 To insert : 987515219 To insert : 987515246 To insert : 987515247 To insert : 987515248 To insert : 987515249 To insert : 987515250 To insert : 987515175 To insert : 987515176 To insert : 987515177 To insert : 987515178 To insert : 987515179 To insert : 987515180 To insert : 987515181 To insert : 987515182 To insert : 987515183 To insert : 987515193 To insert : 987515195 To insert : 987515196 To insert : 987515198 To insert : 987515200 To insert : 987515201 To insert : 987515202 To insert : 987515204 To insert : 987515205 To insert : 987515224 To insert : 987515226 To insert : 987515227 To insert : 987515228 To insert : 987515230 To insert : 987515220 To insert : 987515222 To insert : 987515223 To insert : 987515188 To insert : 987515189 To insert : 987515190 To insert : 987515192 To insert : 987515231 To insert : 987515232 To insert : 987515233 To insert : 987515234 To insert : 987515235 To insert : 987515236 To insert : 987515237 To insert : 987515239 To insert : 987515240 To insert : 987515241 To insert : 987515242 To insert : 987515243 To insert : 987515244 To insert : 987515245 To insert : 987515184 To insert : 987515185 To insert : 987515186 To insert : 987515187 To insert : 987515207 To insert : 987515208 To insert : 987515209 time to insert the descriptors : 17.270238399505615 time spend for datou_step_exec : 25.45278525352478 time spend to save output : 7.128715515136719e-05 total time spend for step 1 : 25.45285654067993 step2:argmax Wed Apr 23 11:24:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step Argmax ! calculate argmax for thcl : 1528 time spend for datou_step_exec : 0.0012631416320800781 time spend to save output : 1.8596649169921875e-05 total time spend for step 2 : 0.00128173828125 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {'987515238': [('987515238', 'Carton', 0.99957246, 1927, '1528'), 'temp/1745400250_1620143_987515238_e6292cb81e05894cfeb4b99f21a1d3f8.jpg'], '987515211': [('987515211', 'Carton', 0.9734117, 1927, '1528'), 'temp/1745400250_1620143_987515211_72cc7664d45bd40477351b9b764f1500.jpg'], '987515212': [('987515212', 'Carton', 0.98691297, 1927, '1528'), 'temp/1745400250_1620143_987515212_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515213': [('987515213', 'Carton', 0.98692733, 1927, '1528'), 'temp/1745400250_1620143_987515213_b0a038fcb9678ebfd60d9b1f6ec1fc17.jpg'], '987515215': [('987515215', 'Papier_Magazine', 0.99390453, 1927, '1528'), 'temp/1745400250_1620143_987515215_902ef348a7eebb9a8b87f42927347936.jpg'], '987515216': [('987515216', 'Papier_Magazine', 0.97745216, 1927, '1528'), 'temp/1745400250_1620143_987515216_4f7dc21f1d2cd3fcabadc4a6755921e1.jpg'], '987515217': [('987515217', 'Carton', 0.5286438, 1927, '1528'), 'temp/1745400250_1620143_987515217_78877bb2c5760be28518d17f77d1c609.jpg'], '987515219': [('987515219', 'Carton', 0.99936813, 1927, '1528'), 'temp/1745400250_1620143_987515219_c2d417a5ba6ccf7c84527636f8d5eef9.jpg'], '987515246': [('987515246', 'Carton', 0.99923337, 1927, '1528'), 'temp/1745400250_1620143_987515246_671a708f67f2efa19004b8257fc7b9c8.jpg'], '987515247': [('987515247', 'Carton', 0.99966776, 1927, '1528'), 'temp/1745400250_1620143_987515247_e47b65403df916ba909bc9c439b0af73.jpg'], '987515248': [('987515248', 'Carton', 0.98126215, 1927, '1528'), 'temp/1745400250_1620143_987515248_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515249': [('987515249', 'Carton', 0.98131436, 1927, '1528'), 'temp/1745400250_1620143_987515249_a70ad88462a22fb62a120721a42b2d42.jpg'], '987515250': [('987515250', 'Carton', 0.9808141, 1927, '1528'), 'temp/1745400250_1620143_987515250_b2827c9639df69656f23abcc7f2f82d9.jpg'], '987515175': [('987515175', 'Papier_Magazine', 0.99981457, 1927, '1528'), 'temp/1745400250_1620143_987515175_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515176': [('987515176', 'Papier_Magazine', 0.9998149, 1927, '1528'), 'temp/1745400250_1620143_987515176_8b398cba2f448622cd9657f5eb3f9796.jpg'], '987515177': [('987515177', 'Papier_Magazine', 0.9771738, 1927, '1528'), 'temp/1745400250_1620143_987515177_4a54e9967227806219ddf45d256539d8.jpg'], '987515178': [('987515178', 'Carton', 0.8578842, 1927, '1528'), 'temp/1745400250_1620143_987515178_298b3d2bfe0fda6787b59a78e2e68867.jpg'], '987515179': [('987515179', 'Carton', 0.9273364, 1927, '1528'), 'temp/1745400250_1620143_987515179_f7d4d1757a470f4c96dc3541eac88b9e.jpg'], '987515180': [('987515180', 'Carton', 0.9899811, 1927, '1528'), 'temp/1745400250_1620143_987515180_776a5d7d8486ee2961bbe3a0d90f95b5.jpg'], '987515181': [('987515181', 'Carton', 0.9977791, 1927, '1528'), 'temp/1745400250_1620143_987515181_1738c2798fb31152809ecb443ac286d6.jpg'], '987515182': [('987515182', 'Carton', 0.99242073, 1927, '1528'), 'temp/1745400250_1620143_987515182_fe7f29bf6d13e08c3e985f91b5232178.jpg'], '987515183': [('987515183', 'Papier_Magazine', 0.99999225, 1927, '1528'), 'temp/1745400250_1620143_987515183_6aab9ca0421398b4899892c10c2594c6.jpg'], '987515193': [('987515193', 'Papier_Magazine', 0.9993973, 1927, '1528'), 'temp/1745400250_1620143_987515193_1a97fceb4dcbf5821d783b2e00b52fe6.jpg'], '987515195': [('987515195', 'Carton', 0.98463136, 1927, '1528'), 'temp/1745400250_1620143_987515195_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515196': [('987515196', 'Carton', 0.9846555, 1927, '1528'), 'temp/1745400250_1620143_987515196_30ccb89dfe410c445878a7f2819ddc36.jpg'], '987515198': [('987515198', 'Carton', 0.9661352, 1927, '1528'), 'temp/1745400250_1620143_987515198_599e80f444c876f407e94b533c89360b.jpg'], '987515200': [('987515200', 'Carton', 0.98591727, 1927, '1528'), 'temp/1745400250_1620143_987515200_978964436b5d5fb0eeda17e3bfafe889.jpg'], '987515201': [('987515201', 'Carton', 0.99546164, 1927, '1528'), 'temp/1745400250_1620143_987515201_b224d2acdc7fa2bbb134c09db6bca7ce.jpg'], '987515202': [('987515202', 'Carton', 0.991113, 1927, '1528'), 'temp/1745400250_1620143_987515202_3314bd90d1404f31b827d8925abf2d62.jpg'], '987515204': [('987515204', 'Papier_Magazine', 0.9950897, 1927, '1528'), 'temp/1745400250_1620143_987515204_9779c4f9d44360a9c80499e3b01e8a09.jpg'], '987515205': [('987515205', 'Papier_Magazine', 0.99084926, 1927, '1528'), 'temp/1745400250_1620143_987515205_fd4b136d0b3a9a1a347942d7191f6fea.jpg'], '987515224': [('987515224', 'Carton', 0.9084868, 1927, '1528'), 'temp/1745400250_1620143_987515224_e8747b400e713ecbd08d5b75db4d7568.jpg'], '987515226': [('987515226', 'Papier_Magazine', 0.9869751, 1927, '1528'), 'temp/1745400250_1620143_987515226_a18048dca1a77ae086b62cf07759f704.jpg'], '987515227': [('987515227', 'Papier_Magazine', 0.9001389, 1927, '1528'), 'temp/1745400250_1620143_987515227_e9c45a0e576ec9e44c1379c3fc5fec7c.jpg'], '987515228': [('987515228', 'Papier_Magazine', 0.521595, 1927, '1528'), 'temp/1745400250_1620143_987515228_9f1759f20c9e603bccb9f9879d2f0d54.jpg'], '987515230': [('987515230', 'Carton', 0.99940586, 1927, '1528'), 'temp/1745400250_1620143_987515230_846ad925884264181565c81d152a2e94.jpg'], '987515220': [('987515220', 'Carton', 0.9963731, 1927, '1528'), 'temp/1745400250_1620143_987515220_e729f316c4c3b32049adfbaaa336d95c.jpg'], '987515222': [('987515222', 'Carton', 0.99747497, 1927, '1528'), 'temp/1745400250_1620143_987515222_067a027bc7402f969b6277d0dcb47eaa.jpg'], '987515223': [('987515223', 'Carton', 0.9920884, 1927, '1528'), 'temp/1745400250_1620143_987515223_ebb57f09941cd11d7ee45a9368a883c1.jpg'], '987515188': [('987515188', 'Carton', 0.9956508, 1927, '1528'), 'temp/1745400250_1620143_987515188_4116f9906657a69bb76c2fda982037b9.jpg'], '987515189': [('987515189', 'Carton', 0.99779046, 1927, '1528'), 'temp/1745400250_1620143_987515189_8e8590a26f72249d4c2116dffd0cf668.jpg'], '987515190': [('987515190', 'Carton', 0.9763064, 1927, '1528'), 'temp/1745400250_1620143_987515190_d56932bfc6ba2a8c974c691108755017.jpg'], '987515192': [('987515192', 'Papier_Magazine', 0.9999112, 1927, '1528'), 'temp/1745400250_1620143_987515192_b661073b218f5f056833d6af1c617153.jpg'], '987515231': [('987515231', 'Carton', 0.99942005, 1927, '1528'), 'temp/1745400250_1620143_987515231_dbf4cafa71b6db4771c5c8f0c25e9cda.jpg'], '987515232': [('987515232', 'Carton', 0.99924564, 1927, '1528'), 'temp/1745400250_1620143_987515232_38db7950cdb3c674ee0ad65915b021f3.jpg'], '987515233': [('987515233', 'Carton', 0.9833124, 1927, '1528'), 'temp/1745400250_1620143_987515233_a92514bed0e8c5724f2d032d3ab1e2ad.jpg'], '987515234': [('987515234', 'Carton', 0.94382423, 1927, '1528'), 'temp/1745400250_1620143_987515234_2eca3480aed0f8b876242675ad99b666.jpg'], '987515235': [('987515235', 'Papier_Magazine', 0.89196736, 1927, '1528'), 'temp/1745400250_1620143_987515235_87075955a2f76b3948b47ffe1825ecd9.jpg'], '987515236': [('987515236', 'Papier_Magazine', 0.53723377, 1927, '1528'), 'temp/1745400250_1620143_987515236_8b44a98b1aceadad73ed000d65836a9a.jpg'], '987515237': [('987515237', 'Carton', 0.7703657, 1927, '1528'), 'temp/1745400250_1620143_987515237_1183dfa371a457f11ce2b622c7cf9467.jpg'], '987515239': [('987515239', 'Carton', 0.99978334, 1927, '1528'), 'temp/1745400250_1620143_987515239_b3fa6f29636080b5138c8d8c33fea309.jpg'], '987515240': [('987515240', 'Carton', 0.9995216, 1927, '1528'), 'temp/1745400250_1620143_987515240_7829b9b15f1bf128ea4e2c1a39b9f0dd.jpg'], '987515241': [('987515241', 'Carton', 0.9821206, 1927, '1528'), 'temp/1745400250_1620143_987515241_073420d938f5f010ffd5b4353c064e09.jpg'], '987515242': [('987515242', 'Carton', 0.9359228, 1927, '1528'), 'temp/1745400250_1620143_987515242_327abb5215d6fd1f0aad51f53ed8c324.jpg'], '987515243': [('987515243', 'Papier_Magazine', 0.87426394, 1927, '1528'), 'temp/1745400250_1620143_987515243_4375283f3bc5cdaa431c2fc6f17f53a4.jpg'], '987515244': [('987515244', 'Papier_Magazine', 0.81755793, 1927, '1528'), 'temp/1745400250_1620143_987515244_419530eaef5ef868f75c758b94eea4b4.jpg'], '987515245': [('987515245', 'Carton', 0.8659897, 1927, '1528'), 'temp/1745400250_1620143_987515245_757d9d208d5bd4375c5f21f68b699148.jpg'], '987515184': [('987515184', 'Papier_Magazine', 0.99973184, 1927, '1528'), 'temp/1745400250_1620143_987515184_19c8c2177209a285df6014d95fe53f2c.jpg'], '987515185': [('987515185', 'Papier_Magazine', 0.7981476, 1927, '1528'), 'temp/1745400250_1620143_987515185_e172d54457cabee9d7f02ee1300f3ae9.jpg'], '987515186': [('987515186', 'Carton', 0.9847398, 1927, '1528'), 'temp/1745400250_1620143_987515186_797def426440b544aa80dbd63a19234a.jpg'], '987515187': [('987515187', 'Carton', 0.98178446, 1927, '1528'), 'temp/1745400250_1620143_987515187_9f62f98efd3caca0b9c17d27f5c70440.jpg'], '987515207': [('987515207', 'Papier_Magazine', 0.87393147, 1927, '1528'), 'temp/1745400250_1620143_987515207_de216ddb041e249524b0fb2b949064a5.jpg'], '987515208': [('987515208', 'Carton', 0.99171513, 1927, '1528'), 'temp/1745400250_1620143_987515208_a2b90cb74908aa64bbc4aae58f0c5ae8.jpg'], '987515209': [('987515209', 'Carton', 0.96760654, 1927, '1528'), 'temp/1745400250_1620143_987515209_02dfe1ae39f51994652f4a8538844aea.jpg']} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : detect_points list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1321866512298584 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:detect_points Wed Apr 23 11:24:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : 1 To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 1 Inside predict_points step exec : nb paths : 1 treate image : temp/1745400278_1620143_987515173_91fa471b1a04f95b356afdbaf021f623.jpg size of numpy array img : 2408584 scale method : caffe/skimage size of numpy array img_scale : 2408584 (448, 448, 3) nb_h 8 nb_w 8 size of sub images : (224, 224, 3) size of caffe_input : 38535320 (64, 3, 224, 224) time to do the preprocess : 0.04538083076477051 time to do a prediction : 0.3415496349334717 dict_keys(['prob']) shape of output (64, 10, 1, 1) shape of the out_put heatmap (10, 8, 8) number of sub_photos vertical and horizon 8 8 size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) size of heatmap : (8,8) time spend for datou_step_exec : 1.8264923095703125 time spend to save output : 4.76837158203125e-05 total time spend for step 1 : 1.8265399932861328 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {987515173: [(987515173, 1982, 'Autre_Environement', 112, -1, 112, -1, 6.308155386935965e-12), (987515173, 1982, 'Autre_Environement', 144, -1, 112, -1, 2.4386243024920873e-11), (987515173, 1982, 'Autre_Environement', 176, -1, 112, -1, 1.062672527751829e-08), (987515173, 1982, 'Autre_Environement', 208, -1, 112, -1, 4.4643701357927057e-07), (987515173, 1982, 'Autre_Environement', 240, -1, 112, -1, 1.9260387489339337e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 112, -1, 3.7828136555617675e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 112, -1, 0.00012278616486582905), (987515173, 1982, 'Autre_Environement', 336, -1, 112, -1, 2.948332803498488e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 144, -1, 2.3579364238912603e-08), (987515173, 1982, 'Autre_Environement', 144, -1, 144, -1, 2.2059175819322263e-08), (987515173, 1982, 'Autre_Environement', 176, -1, 144, -1, 1.3808123355829593e-07), (987515173, 1982, 'Autre_Environement', 208, -1, 144, -1, 1.4745874068466946e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 144, -1, 1.1336028364894446e-05), (987515173, 1982, 'Autre_Environement', 272, -1, 144, -1, 0.00015862264262977988), (987515173, 1982, 'Autre_Environement', 304, -1, 144, -1, 0.0004440613556653261), (987515173, 1982, 'Autre_Environement', 336, -1, 144, -1, 6.542903429362923e-05), (987515173, 1982, 'Autre_Environement', 112, -1, 176, -1, 1.3320421885509859e-06), (987515173, 1982, 'Autre_Environement', 144, -1, 176, -1, 1.621295382392418e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 176, -1, 2.5273734536312986e-06), (987515173, 1982, 'Autre_Environement', 208, -1, 176, -1, 1.6172660934898886e-06), (987515173, 1982, 'Autre_Environement', 240, -1, 176, -1, 6.271816801017849e-06), (987515173, 1982, 'Autre_Environement', 272, -1, 176, -1, 8.742031059227884e-05), (987515173, 1982, 'Autre_Environement', 304, -1, 176, -1, 0.0003267963184043765), (987515173, 1982, 'Autre_Environement', 336, -1, 176, -1, 0.0003053733380511403), (987515173, 1982, 'Autre_Environement', 112, -1, 208, -1, 1.8643100702320226e-05), (987515173, 1982, 'Autre_Environement', 144, -1, 208, -1, 7.927898877824191e-06), (987515173, 1982, 'Autre_Environement', 176, -1, 208, -1, 2.705913721001707e-05), (987515173, 1982, 'Autre_Environement', 208, -1, 208, -1, 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0.0007317812414839864)]} ############################### TEST certificat_qualite_papier ################################ TEST certificat qualite papier Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 4442 tile have less inputs used (1) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 4441 detect_points is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 4443 count_percent_refus is not consistent : 4 used against 3 in the step definition ! Step 4444 send_mail_dechet have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 1 of step 4440 have datatype=1 whereas input 0 of step 4443 have datatype=2 WARNING : type of output 1 of step 4441 doesn't seem to be define in the database( WARNING : type of input 4 of step 4443 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : init_dechet, tile, detect_points, count_percent_refus, brightness, blur_detection, send_mail_dechet list_input_json : [] origin Catched exception ! Connect or reconnect ! We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18646764755249023 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:init_dechet Wed Apr 23 11:24:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec debut step init detect dechets input : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg ON MODIFIE NB AVEC LE INPUT map photo id path extension : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg scale : 0.9481481481481482 FIN step init dechet Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : init_dechet we use saveGeneral [987321136] Looping around the photos to save general results len do output : 1 /987321136Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.016768693923950195 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.00022077560424804688 time spend to save output : 0.01706981658935547 total time spend for step 1 : 0.017290592193603516 step2:tile Wed Apr 23 11:24:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure verbose : False param_json : {'token': '78d09a0790ec6ecbf119343125a81fdc', 'portfolio_name': 'tile_correct_upm', 'ETA': 86400, 'new_width': 1500, 'new_height': 20000, 'host': 'www.fotonower.com', 'protocol': 'https', 'photo_tile_type': 1522, 'option_bande': 'True'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {406: 410} map_filenames : {987321136: 'temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 1 batch 1 Loaded 0 chid ids of type : 0 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=tile_correct_upm&access_token=78d09a0790ec6ecbf119343125a81fdc created feed_id_new_photos : 22341567 with name tile_correct_upm feed_id_new_photos : 22341567 filename : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg photo_id : 987321136 height_image_input : 439 width_image_input : 562 new_width : 1500 new_height : 20000 stride : 0 stride_relative : 0.1 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 0 list_bib_to_crops : 1 [(0, 562, 0, 439, 0)] new_crops_tiles : 1 crop_transformed : 0 batch 1 Loaded 1 chid ids of type : 1522 treat the image : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg , 0 before upload mediasElapsed time : 0.01200103759765625 About to upload 1 photos upload in portfolio : 22341567 Result OK ! uploaded one batch 0 Elapsed time : 4.890162229537964 upload mediasElapsed time : 4.902264356613159 , 0Saving 0 CHIs. end of tileElapsed time : 4.921022415161133 Inside saveOutput : final : False verbose : False saveOutput not yet implemented for datou_step.type : tile we use saveGeneral [987321136, 987321136, '1353002373'] Looping around the photos to save general results len do output : 1 /1353002373Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1353002373', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.013693571090698242 save_final save missing photos in datou_result : time spend for datou_step_exec : 11.729635953903198 time spend to save output : 0.013969659805297852 total time spend for step 2 : 11.743605613708496 step3:detect_points Wed Apr 23 11:24:52 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step predict points ! Inside try reload ! gpu_mode in detect_points : False To load net FromThcl() model_param file didn't exist model_name : learn_refus_upm_blanches_1924 model_type : caffe list file need : ['caffemodel', 'deploy_conv_normal.prototxt', 'deploy_fc.prototxt', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file exist in s3 : ['caffemodel', 'deploy.prototxt', 'mean.npy', 'synset_words.txt'] file manque in s3 : ['deploy_conv_normal.prototxt', 'deploy_fc.prototxt'] local folder : /data/models_weight/learn_refus_upm_blanches_1924 /data/models_weight/learn_refus_upm_blanches_1924/caffemodel size_local : 45774543 size in s3 : 45774543 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 caffemodel already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/deploy.prototxt size_local : 17312 size in s3 : 17312 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:03 deploy.prototxt already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/mean.npy size_local : 1572992 size in s3 : 1572992 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:05 mean.npy already exist and didn't need to update /data/models_weight/learn_refus_upm_blanches_1924/synset_words.txt size_local : 218 size in s3 : 218 create time local : 2021-08-09 05:29:53 create time in s3 : 2021-08-06 19:36:04 synset_words.txt already exist and didn't need to update reshape net's input to : (224, 224) origin shape : (10, 3, 224, 224) after reshape : (1, 3, 224, 224) [('data', (1, 3, 224, 224)), ('conv1', (1, 64, 112, 112)), ('pool1', (1, 64, 56, 56)), ('pool1_pool1_0_split_0', (1, 64, 56, 56)), ('pool1_pool1_0_split_1', (1, 64, 56, 56)), ('res2a_branch1', (1, 64, 56, 56)), ('res2a_branch2a', (1, 64, 56, 56)), ('res2a_branch2b', (1, 64, 56, 56)), ('res2a', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_0', (1, 64, 56, 56)), ('res2a_res2a_relu_0_split_1', (1, 64, 56, 56)), ('res2b_branch2a', (1, 64, 56, 56)), ('res2b_branch2b', (1, 64, 56, 56)), ('res2b', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_0', (1, 64, 56, 56)), ('res2b_res2b_relu_0_split_1', (1, 64, 56, 56)), ('res3a_branch1', (1, 128, 28, 28)), ('res3a_branch2a', (1, 128, 28, 28)), ('res3a_branch2b', (1, 128, 28, 28)), ('res3a', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_0', (1, 128, 28, 28)), ('res3a_res3a_relu_0_split_1', (1, 128, 28, 28)), ('res3b_branch2a', (1, 128, 28, 28)), ('res3b_branch2b', (1, 128, 28, 28)), ('res3b', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_0', (1, 128, 28, 28)), ('res3b_res3b_relu_0_split_1', (1, 128, 28, 28)), ('res4a_branch1', (1, 256, 14, 14)), ('res4a_branch2a', (1, 256, 14, 14)), ('res4a_branch2b', (1, 256, 14, 14)), ('res4a', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_0', (1, 256, 14, 14)), ('res4a_res4a_relu_0_split_1', (1, 256, 14, 14)), ('res4b_branch2a', (1, 256, 14, 14)), ('res4b_branch2b', (1, 256, 14, 14)), ('res4b', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_0', (1, 256, 14, 14)), ('res4b_res4b_relu_0_split_1', (1, 256, 14, 14)), ('res5a_branch1', (1, 512, 7, 7)), ('res5a_branch2a', (1, 512, 7, 7)), ('res5a_branch2b', (1, 512, 7, 7)), ('res5a', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_0', (1, 512, 7, 7)), ('res5a_res5a_relu_0_split_1', (1, 512, 7, 7)), ('res5b_branch2a', (1, 512, 7, 7)), ('res5b_branch2b', (1, 512, 7, 7)), ('res5b', (1, 512, 7, 7)), ('fc2019-10-22_15-02-46', (1, 10, 1, 1)), ('prob', (1, 10, 1, 1))] set image transformer : About to compute detect the points : len(args) : 2 Inside predict_points step exec : nb paths : 1 treate image : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea_0.jpg size of numpy array img : 2960752 scale method : caffe/skimage size of numpy array img_scale : 2655880 (416, 532, 3) nb_h 7 nb_w 11 size of sub images : (224, 224, 3) size of caffe_input : 46362776 (77, 3, 224, 224) time to do the preprocess : 0.05517101287841797 time to do a prediction : 15.320726156234741 dict_keys(['prob']) shape of output (77, 10, 1, 1) shape of the out_put heatmap (10, 7, 11) number of sub_photos vertical and horizon 7 11 size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) size of heatmap : (7,11) Inside saveOutput : final : False verbose : False Inside savePoints : final : False verbose : False threshold to save the result : 0.05 maximun points to save in the table mtr_datou_result for each class : 100 final : False save missing photos in datou_result : time spend for datou_step_exec : 16.61711621284485 time spend to save output : 0.06746864318847656 total time spend for step 3 : 16.684584856033325 step4:count_percent_refus Wed Apr 23 11:25:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure debut step count percent refus (987321136, 0.9481481481481482) ('temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea_0.jpg',) list_photo : [987321136] list_photo_correc : [1353002373] debut step count percent refus Treating photo_id : 987321136 Calcul du count_res count res : ((492774966, 3), (2107752386, 7)) Hashtag_id : 492774966 Hashtag_id : 2107752386 We have 2 classes in this image Inside saveOutput : final : False verbose : False begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.050729990005493164 save missing photos in datou_result : time spend for datou_step_exec : 0.017357587814331055 time spend to save output : 0.05087566375732422 total time spend for step 4 : 0.06823325157165527 step5:brightness Wed Apr 23 11:25:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness treat image : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.021803617477416992 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.010885000228881836 save missing photos in datou_result : time spend for datou_step_exec : 0.09985136985778809 time spend to save output : 0.037862300872802734 total time spend for step 5 : 0.13771367073059082 step6:blur_detection Wed Apr 23 11:25:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection methode: ratio et variance treat image : temp/1745400280_1620143_987321136_6a08497399a24a3041045c21475a90ea.jpg resize: (439, 562) 987321136 -5.392404060312662 Inside saveOutput : final : False verbose : False begin to insert list_values into class_photo_scores : length of list_valuse in save_photo_hashtag_id_thcl_score : 1 time used for this insertion : 0.009032964706420898 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.009972333908081055 save missing photos in datou_result : time spend for datou_step_exec : 0.1572866439819336 time spend to save output : 0.11602973937988281 total time spend for step 6 : 0.2733163833618164 step7:send_mail_dechet Wed Apr 23 11:25:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail dechet senders@fotonower.com retour de l'envoi du mail : None Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : send_mail_dechet we use saveGeneral [987321136, 987321136, '1353002373'] Looping around the photos to save general results len do output : 1 /987321136. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', '1902940', '987321136', None, None, None, None, None, None) ('1848', None, None, None, None, None, None, None, None) ('1848', None, '1353002373', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 4 time used for this insertion : 0.01773548126220703 save_final save missing photos in datou_result : time spend for datou_step_exec : 0.4135103225708008 time spend to save output : 0.018166542053222656 total time spend for step 7 : 0.43167686462402344 caffe_path_current : About to save ! 2 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {987321136: (-110, -0.39870825574700136, -5.392404060312662, 30.0, 61.64383561643836, {'carton': 3, 'Papier_Magazine': 7}, {'refus_total': 30.0, 'carton': 30.0, 'Papier_Magazine': 70.0}, {'refus_total': 61.64383561643836, 'carton': 61.64383561643836, 'Papier_Magazine': 38.35616438356164}, 0.6164383561643836)} ############################### TEST image_temperature_detection ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_temperature_detection list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.26639342308044434 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_temperature_detection Wed Apr 23 11:25:09 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanche_jaune_detection treat image : temp/1745400309_1620143_984484223_2e25dc219a9a57a9f85bcae482a80c35.jpg 984484223 1.004309911525615 time spend for datou_step_exec : 0.163316011428833 time spend to save output : 4.839897155761719e-05 total time spend for step 1 : 0.16336441040039062 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {984484223: [(984484223, 1.004309911525615, 492630606)]} {984484223: [(984484223, 1.004309911525615, 492630606)]} ############################### TEST broca ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.015325546264648438 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:split_time_score Wed Apr 23 11:25:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec split portfolio by speed calcul order for each photo with time calcul time for a portfolio 2021-12-01 10:11:30 2021-12-01 10:11:32 2021-12-01 10:11:30 2021-12-01 10:11:34 2021-12-01 10:11:32 2021-12-01 10:11:40 2021-12-01 10:11:34 2021-12-01 10:12:17 2021-12-01 10:11:40 2021-12-01 10:12:24 2021-12-01 10:12:17 2021-12-01 10:12:27 2021-12-01 10:12:24 2021-12-01 10:12:29 2021-12-01 10:12:27 2021-12-01 10:12:56 2021-12-01 10:12:29 2021-12-01 10:13:04 2021-12-01 10:12:56 2021-12-01 10:13:13 2021-12-01 10:13:04 2021-12-01 10:13:04 distance 1.4513659170185111 2021-12-01 10:13:13 2021-12-01 10:13:22 2021-12-01 10:13:13 2021-12-01 10:13:30 2021-12-01 10:13:22 2021-12-01 10:16:14 2021-12-01 10:13:30 2021-12-01 10:13:30 distance 8.382409567451603 2021-12-01 10:16:14 2021-12-01 10:16:18 2021-12-01 10:16:14 2021-12-01 10:16:47 2021-12-01 10:16:18 2021-12-01 10:16:53 2021-12-01 10:16:47 2021-12-01 10:16:47 distance 8.03396608896571 2021-12-01 10:16:53 2021-12-01 10:16:57 2021-12-01 10:16:53 dict_time_useful: {0: [1098136690, 1098136784, 48.864288393888884, 2.19199505125, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94]], 1: [1098136974, 1098137007, 48.86291258986111, 2.19361357125, [datetime.datetime(2021, 12, 1, 10, 16, 14), datetime.datetime(2021, 12, 1, 10, 16, 47), 33]]} get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "CS"; get gps info of PAV SELECT id,Y_WGS84,X_WGS84 FROM MTRLabel.info_PAV WHERE type_pav = "OM"; distance: RUEIL14CS [48.864288393888884, 2.19199505125] 16.57008455321128 time spend for datou_step_exec : 0.16346025466918945 time spend to save output : 0.00010442733764648438 total time spend for step 1 : 0.16356468200683594 caffe_path_current : About to save ! 0 After save, about to update current ! {15: [(22341593, 48.864288393888884, 2.19199505125, 10, 1064919752, [datetime.datetime(2021, 12, 1, 10, 11, 30), datetime.datetime(2021, 12, 1, 10, 13, 4), 94.0], 5205529)]} résultat du premier test BROCA : True True ############################### TEST crop_conditional ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 1335 frcnn is not linked in the step_by_step architecture ! WARNING : step 1336 crop_condition is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! List Step Type Loaded in datou : frcnn, crop_condition list_input_json : [] origin We have 1 , BBBFBFBFBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 6 ; length of list_pids : 6 ; length of list_args : 6 time to download the photos : 0.30939555168151855 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 2 step1:frcnn Wed Apr 23 11:25:10 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou step Faster rcnn ! Inside try reload ! To loadFromThcl() model_param file didn't exist model_name : learn_piece_voiture_0808_v2 model_type : caffe_faster_rcnn list file need : ['caffemodel', 'test.prototxt'] file exist in s3 : ['caffemodel', 'test.prototxt'] file manque in s3 : [] local folder : /data/models_weight/learn_piece_voiture_0808_v2 /data/models_weight/learn_piece_voiture_0808_v2/caffemodel size_local : 350215080 size in s3 : 350215080 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 caffemodel already exist and didn't need to update /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt size_local : 7166 size in s3 : 7166 create time local : 2021-08-09 05:30:22 create time in s3 : 2021-08-06 19:24:16 test.prototxt already exist and didn't need to update prototxt : /data/models_weight/learn_piece_voiture_0808_v2/test.prototxt caffemodel : /data/models_weight/learn_piece_voiture_0808_v2/caffemodel Loaded network /data/models_weight/learn_piece_voiture_0808_v2/caffemodel About to compute detect_faster_rcnn : len(args) : 6 Inside frcnn step exec : nb paths : 6 image_path : temp/1745400310_1620143_950003838_e480bc28e6ceabc2f5995246a6af6b46.jpg image_size (294, 285, 3) [[[ 29 29 29] [ 29 29 29] [ 30 30 30] ... [182 172 165] [141 131 124] [103 94 90]] [[ 29 29 29] [ 29 29 29] [ 31 31 31] ... [231 220 212] [202 193 184] [164 154 147]] [[ 30 30 30] [ 27 27 27] [ 26 26 26] ... [223 211 199] [229 219 209] [228 217 209]] ... [[ 22 27 25] [ 16 21 19] [ 11 16 14] ... [166 145 123] [168 147 125] [170 149 127]] [[ 20 25 23] [ 17 22 20] [ 15 20 18] ... [163 142 120] [165 144 122] [166 145 123]] [[ 13 18 16] [ 17 22 20] [ 20 25 23] ... [162 141 119] [163 142 120] [163 142 121]]] Detection took 0.214s for 300 object proposals image_path : temp/1745400310_1620143_950003813_e28be02dfcce79cce594a390a9911a0a.jpg image_size (254, 229, 3) [[[202 190 186] [205 193 189] [205 194 190] ... [ 81 70 56] [ 80 69 55] [ 78 67 53]] [[198 187 183] [200 189 185] [198 189 185] ... [ 50 41 28] [ 44 36 23] [ 45 36 23]] [[192 187 184] [191 186 183] [191 186 183] ... [ 36 30 23] [ 32 29 21] [ 33 27 20]] ... [[187 186 190] [186 185 189] [188 184 189] ... [ 43 38 35] [ 37 33 28] [ 33 28 25]] [[184 185 189] [183 184 188] [184 183 187] ... [ 28 23 22] [ 29 24 21] [ 33 28 27]] [[181 185 186] [180 184 185] [182 184 185] ... [ 23 15 16] [ 22 14 14] [ 24 16 17]]] Detection took 0.053s for 300 object proposals image_path : temp/1745400310_1620143_950003695_22b4110c9a86b12e1542ec2bb977f6a8.jpg image_size (2160, 3840, 3) [[[111 118 91] [113 120 93] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 24 41 38]] [[111 118 91] [112 119 92] [115 120 93] ... [ 23 40 37] [ 23 40 37] [ 23 40 37]] [[113 118 91] [114 119 92] [115 120 93] ... [ 22 39 36] [ 23 40 37] [ 23 40 37]] ... [[120 125 94] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 23 37 35]] [[119 124 93] [119 124 93] [118 123 92] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]] [[118 123 91] [117 122 90] [117 122 91] ... [ 22 36 34] [ 22 36 34] [ 22 36 34]]] Detection took 0.405s for 300 object proposals image_path : temp/1745400310_1620143_926687666_a8bc8c1fad77748c62ca641ceb29ad9c.jpg image_size (480, 640, 3) [[[36 41 44] [36 41 44] [35 40 43] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[37 42 45] [36 41 44] [35 40 43] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[37 42 45] [36 41 44] [35 40 43] ... [ 3 5 5] [ 4 6 6] [ 4 6 6]] ... [[42 47 50] [41 46 49] [40 45 48] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[41 46 49] [41 46 49] [40 45 48] ... [ 0 2 2] [10 12 12] [22 24 24]] [[40 45 48] [40 45 48] [40 45 48] ... [10 12 12] [17 19 19] [26 28 28]]] Detection took 0.035s for 300 object proposals image_path : temp/1745400310_1620143_950003812_3dbffe9f441f7d28d087f3e571769e74.jpg image_size (480, 614, 3) [[[ 44 44 44] [ 49 51 51] [ 42 44 44] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 43 43 43] [ 36 38 38] [ 39 41 41] ... [ 5 7 7] [ 5 7 7] [ 5 7 7]] [[ 70 70 70] [ 40 42 42] [ 41 43 43] ... [ 4 6 6] [ 4 6 6] [ 4 6 6]] ... [[103 101 101] [110 108 108] [ 61 59 59] ... [ 8 10 10] [ 8 10 10] [ 8 10 10]] [[ 98 96 96] [115 113 113] [ 73 71 71] ... [ 0 2 2] [ 11 13 13] [ 21 23 23]] [[ 92 90 90] [114 112 112] [ 87 82 83] ... [ 10 12 12] [ 18 20 20] [ 25 27 27]]] Detection took 0.052s for 300 object proposals image_path : temp/1745400310_1620143_950003696_11e3a77b72af4b332d366d98984039c7.jpg image_size (2160, 3264, 3) [[[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 48 60 64] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 48 60 64]] [[168 165 161] [168 165 161] [168 165 161] ... [ 47 59 63] [ 47 59 63] [ 47 59 63]] ... [[167 164 160] [167 164 160] [167 164 160] ... [ 44 59 61] [ 44 59 61] [ 44 59 61]] [[165 162 158] [165 162 158] [165 162 158] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]] [[164 161 157] [164 161 157] [164 161 157] ... [ 45 60 62] [ 45 60 62] [ 45 60 62]]] Detection took 0.417s for 300 object proposals len de result frcnn : 6 time spend for datou_step_exec : 3.4944775104522705 time spend to save output : 0.0007212162017822266 total time spend for step 1 : 3.4951987266540527 step2:crop_condition Wed Apr 23 11:25:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 757 Loading chi in step crop for subpids : 6 ! batch 1 Loaded 32 chid ids of type : 757 begin to crop the class : phare param for this class : {'margin_type': 'margin', 'margin_value': 300, 'feed_id_new_photos': 1097966} filtre for class : phare hashtag_id of this class : 492624020 WARNING : margin is only used for type bib ! map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : phare begin to crop the class : aile-avant param for this class : {} filtre for class : aile-avant hashtag_id of this class : 2106233860 WARNING : margin is only used for type bib ! now we use margin_relative for the photo_id : 926687666 now we use margin_relative for the photo_id : 950003812 map_result returned by crop_photo_return_map_crop : length : 2 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : aile-avant time spend for datou_step_exec : 0.4227440357208252 time spend to save output : 5.173683166503906e-05 total time spend for step 2 : 0.42279577255249023 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 2 output : {1071808962: [926687666, 'temp/1745400310_1620143_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713621_0.jpg', (326, 477, 251, 312)], 1071808957: [950003812, 'temp/1745400310_1620143_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713647_0.jpg', (318, 489, 264, 310)], 1071808960: [950003812, 'temp/1745400310_1620143_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713648_0.jpg', (261, 408, 234, 331)], 1071808969: [926687666, 'temp/1745400310_1620143_926687666_a8bc8c1fad77748c62ca641ceb29ad9c_bib_crop_1655713607_0.jpg', (161, 330, 149, 343)], 1071808966: [950003812, 'temp/1745400310_1620143_950003812_3dbffe9f441f7d28d087f3e571769e74_bib_crop_1655713634_0.jpg', (133, 305, 146, 344)]} ############################### TEST image_blanchir ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : image_blanchir list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14574241638183594 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! WARNING : we have an input that is not a photo, we should get rid of it Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:image_blanchir Wed Apr 23 11:25:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec inside step blanchir_image https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos:22341594 treat image : temp/1745400314_1620143_990111206_7ca22c7e68dd0a10509c7987af0cf549.png blanchir func Result OK ! time spend for datou_step_exec : 7.069112062454224 time spend to save output : 6.198883056640625e-06 total time spend for step 1 : 7.06911826133728 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[1818, 0, 990111206, 1, 1, 1, None, 1, None]] time used for this insertion : 0.011025190353393555 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(990111206, '1353002405', 0, 300, 0, 381, 1, 1, 'blanc')] [(990111206, '1353002405', 0, 300, 0, 381, 1, 1, 'blanc')] ############################### TEST darker_image ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : darker_image list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14156675338745117 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:darker_image Wed Apr 23 11:25:22 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec dans la step darker batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points treat image : temp/1745400322_1620143_989962950_4d2e56be59e275c3d57b085a836be0ba.jpg Result OK ! batch 1 Loaded 7 chid ids of type : 2228 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! crops sauvegardes time spend for datou_step_exec : 7.58606481552124 time spend to save output : 2.9802322387695312e-05 total time spend for step 1 : 7.586094617843628 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2085, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.012949705123901367 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, '1353002412', 0, 897, 0, 1431, 1, 1, 'darker')] [(989962950, '1353002412', 0, 897, 0, 1431, 1, 1, 'darker')] batch 1 Loaded 7 chid ids of type : 2228 ############################### TEST img_aug ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : data_aug list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.14508557319641113 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:data_aug Wed Apr 23 11:25:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec numpy.version est ancienne, on utilise l'ancien bit generator numpy.version est ancienne, on utilise l'ancien bit generator batch 1 Loaded 7 chid ids of type : 2228 +WARNING : Unexpected points, we should remove this data for chi_id : 1753484977, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484978, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484979, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484980, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484981, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484982, for now we just ignore these empty polygon points +WARNING : Unexpected points, we should remove this data for chi_id : 1753484983, for now we just ignore these empty polygon points on traite des points Result OK ! batch 1 Loaded 7 chid ids of type : 2260 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492774966 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492725882 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1353002421 ERROR missing MTRPhoto.crop_hashtag_ids : 492668766 on photo_id : 1353002421 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! photo_uploade augmentation faite pour la photo : 989962950 time spend for datou_step_exec : 7.199705123901367 time spend to save output : 4.744529724121094e-05 total time spend for step 1 : 7.199752569198608 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False sauvegarde pour la step blanchir_image begin to insert list_values into mtr_datou_result : length of list_values in save_final : 1 insert ignore into MTRPhoto.mtr_datou_result (mtd_id, mtr_portfolio_id,mtr_photo_id,result,result_long,result_double,hashtag_id,proba, mtr_current_id) values (%s,%s,%s,%s,%s,%s,%s,%s,%s) on duplicate key update mtr_portfolio_id = mtr_portfolio_id list_values : [[2041, 0, 989962950, 1, 1, 1, None, 1, None]] time used for this insertion : 0.01194000244140625 save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : [(989962950, 1353002421, 0, 1431, 0, 897, 1, 1, 'img_aug')] [(989962950, 1353002421, 0, 1431, 0, 897, 1, 1, 'img_aug')] batch 1 Loaded 7 chid ids of type : 2260 ############################### TEST rubbia ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.02342820167541504 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Wed Apr 23 11:25:38 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec begin split time score 2022-04-13 10:29:59 0 TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('05', 2), ('07', 25), ('06', 1), ('08', 96), ('09', 44), ('10', 64)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {1: 188, 2: 36, 3: 8} 07092021 4599398 Nombre de photos uploadées : 232 / 23040 (1%) 07092021 4599398 Nombre de photos taguées (types de déchets): 232 / 232 (100%) 07092021 4599398 Nombre de photos taguées (volume) : 232 / 232 (100%) elapsed_time : load_data_split_time_score 5.4836273193359375e-06 elapsed_time : order_list_meta_photo_and_scores 0.0002548694610595703 elapsed_time : fill_and_build_computed_from_old_data 0.0324249267578125 elapsed_time : insert_dashboard_record_day_entry 0.029830217361450195 Creating list_photo_total elapsed_time : select_descriptors 19.172515630722046 07092021 4599398 Nombre de photos avec descriptors (type 3963) : 232 / 232 (100%) ERROR : Hum hum, what can we do for different size of descriptors (ignore the difference ) : 0 vs 2048 photo_id : 1049293230 photo_id_prec : 0 0:00:00|ON:0:27:28.999934|OFF:1:46:59.999878|ON:0:00:20.000007|OFF:0:01:51.000162|ON:0:12:18.999909|OFF:0:01:01.000055|ON:0:08:50.000116|OFF:0:00:09.999867|ON:0:00:19.999899|OFF:0:00:09.000058|ON:0:00:29.999860|OFF:0:01:40.000249|ON:0:00:30.999931|OFF:0:07:40.000107|ON:0:00:28.999981|OFF:0:00:09.999968|ON:0:00:10.999986|OFF:0:08:09.999919|ON:0:00:40.000176|OFF:0:01:08.999784|ON:0:00:11.000245|OFF:0:00:39.999921|ON:0:00:19.000004|OFF:0:06:31.000039|ON:0:02:09.999929|OFF:0:01:40.000021|ON:0:00:39.000031|OFF:0:07:10.999966|ON:0:12:30.000101|OFF:0:00:18.999765|ON:0:00:39.999946|OFF:0:00:11.000212|ON:0:00:29.999851|OFF:0:00:20.000150|ON:0:00:30.000042|OFF:0:00:18.999771|ON:0:07:31.000243|OFF:0:00:09.999942|ON:0:00:08.999822|OFF:0:00:11.000172|ON:0:00:39.999914|OFF:0:00:20|ON:0:31:10.000147|OFF:0:12:18.999857|ON:0:01:39.999950|OFF:0:00:19.999947|ON:0:00:21.000213|OFF:0:00:28.999911|ON:0:00:21.000117|OFF:0:00:40.000020|ON:0:10:58.999762|OFF:0:00:41.000023|ON:0:00:09.000008|OFF:0:00:21.000234|ON:0:00:29.999765|OFF:0:00:28.999920|ON:0:00:21.000174|OFF:0:00:30.000078|ON:0:00:29.999938|OFF:0:00:29.999871|ON:0:00:08.999965|OFF:0:09:31.000234|ON:0:00:09.999916|OFF:0:00:20.000049|ON:0:04:09.999926|OFF:0:01:09.000014|ON:0:02:00.999957|OFF:0:00:08.999951|ON:0:00:21.000053|OFF:0:00:18.999927|ON:0:00:39.999997|OFF:0:00:30.000158|ON: 07092021 Removing 115 photos because of the 'same image' condition Total on : 7859.999814999999 list_time_on Total off : 10509.0002 list_time_off dist_desc begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 232 time used for this insertion : 0.04821658134460449 photos_removed : len 115 elapsed_time : remove_photo_duplicate 0.13287091255187988 Creating list_photo_total XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX elapsed_time : count_sum_diff_and_build_graph 0.060827016830444336 Total photos : 232 ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 10 hashtag : 2107750911 photo_id =1049308384 : rungis_plastique_fonce ..can't find max_score_info ...can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ....can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .....Change port : 25 hashtag : 2107750908 photo_id =1049311795 : rungis_papier ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 4 hashtag : 2107750911 photo_id =1049311961 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 1 hashtag : 2107750908 photo_id =1049312208 : rungis_papier .....Change port : 5 hashtag : 2107750911 photo_id =1049312420 : rungis_plastique_fonce .Change port : 1 hashtag : 2107750908 photo_id =1049312422 : rungis_papier ..can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049312438 : rungis_plastique_fonce ....can't find max_score_info ...can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info ....Change port : 12 hashtag : 2107750908 photo_id =1049312556 : rungis_papier .can't find max_score_info ..can't find max_score_info .....can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750911 photo_id =1049312984 : rungis_plastique_fonce ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .........can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 17 hashtag : 2107751280 photo_id =1049317359 : rungis_aluminium .can't find max_score_info .can't find max_score_info ....can't find max_score_info .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info ...Change port : 8 hashtag : 2107750913 photo_id =1049317524 : rungis_tetrapak .can't find max_score_info .can't find max_score_info .can't find max_score_info ..can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info .Change port : 2 hashtag : 2107750911 photo_id =1049318212 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info ...can't find max_score_info .can't find max_score_info .can't find max_score_info .can't find max_score_info ..........Change port : 12 hashtag : 2107750908 photo_id =1049318287 : rungis_papier ...can't find max_score_info ..Change port : 4 hashtag : 2107750911 photo_id =1049318294 : rungis_plastique_fonce .can't find max_score_info .can't find max_score_info .....can't find max_score_info .can't find max_score_info .can't find max_score_info . Total photos : 232 Number of lists : 15 counter photos in port : 117 hashtag : rungis_aluminium(2107751280) : 8 photos in 1 portfolios ! hashtag : rungis_carton(2107750907) : 0 photos in 0 portfolios ! hashtag : rungis_papier(2107750908) : 33 photos in 6 portfolios ! hashtag : rungis_plastique_clair(2107750909) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_dur(2107750910) : 0 photos in 0 portfolios ! hashtag : rungis_plastique_fonce(2107750911) : 74 photos in 7 portfolios ! hashtag : rungis_tapis_vide(2107750912) : 0 photos in 0 portfolios ! hashtag : rungis_tetrapak(2107750913) : 2 photos in 1 portfolios ! elapsed_time : group_photo_by_moyenne_exp 0.013580083847045898 elapsed_time : compute_and_correct_tag_with_moyenne_mobile 3.814697265625e-06 today str has not a value , we define it as the date of the first image todaystr_first : 07092021 attention , prev_timestamp is 0 , we do nothing *******o** BIG TIME 550.0000100135803 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049307693 2021-09-07 07:45:54.010041 id_data : 12 * BIG TIME 168.99982810020447 (11.000229120254517, 2, 0, 0, 0.979349, 0, 0, 0, 0, 0, 0.8199999980926513, 0.0012, 0.0008999778032302856, 2.9, 0.11100016188621521, -0.0) on 3 1049308235 2021-09-07 07:48:43.009869 id_data : 13 ** BIG TIME 499.9998118877411 (191.00026988983154, 2, 0, 0, 0.93498826, 0, 0, 0, 0, 0, 0.9018999995946884, 0.002, 0.002999984407424927, 2.9, 0.019000051021575926, -0.01666705330212911) on 7 1049309345 2021-09-07 07:58:43.009858 id_data : 20 * BIG TIME 371.0001440048218 (271.00081276893616, 5, 0.24365342, 0, 0, 0, 0, 0.38418204, 0, 0.34177557, 0.9720000085830689, 0.0041, 0.014999971222877502, 2.9, 0.061000473976135255, -0.2516670016447703) on 15 1049310132 2021-09-07 08:09:54.010082 id_data : 51 * BIG TIME 461.0001001358032 (301.0006546974182, 0, 0.5752453, 0, 0, 0, 0, 0, 0, 0, 1.0228999980926514, 0.0056, 0.02589997522830963, 2.9, 0.009999968051910401, -0.0) on 18 1049310905 2021-09-07 08:18:54.009936 id_data : 60 * BIG TIME 370.0001759529114 (359.0003435611725, 5, 0, 0, 0, 0, 0, 0.85983855, 0, 0, 1.0898999773979188, 0.0078, 0.04109999623298645, 2.9, 0.008999762058258056, -0.0) on 24 1049311767 2021-09-07 08:28:24.010105 id_data : 81 *** BIG TIME 411.0001149177551 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312208 2021-09-07 08:40:04.010052 id_data : 97 * BIG TIME 549.9999330043793 (557.9999935626984, 2, 0, 0, 0.66983944, 0, 0, 0, 0, 0, 1.1558999848842622, 0.0094, 0.050199996829032895, 2.9, 0.00900000500679016, -0.0) on 32 1049312363 2021-09-07 08:49:14.009985 id_data : 98 ** BIG TIME 168.99987387657166 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312508 2021-09-07 08:58:23.009966 id_data : 123 * BIG TIME 259.99999809265137 (867.0004575252533, 2, 0, 0, 0.5498895, 0, 0, 0, 0, 0.29987606, 1.293000004196167, 0.0123, 0.06009994525909424, 0.9, 0.011000201940536499, -0.0) on 49 1049312556 2021-09-07 09:02:43.009964 id_data : 124 * BIG TIME 190.00016593933105 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312803 2021-09-07 09:07:34.010149 id_data : 135 * BIG TIME 180.0000081062317 (929.000762462616, 5, 0, 0, 0.44379362, 0, 0, 0.54574114, 0, 0, 1.3459999933004378, 0.0135, 0.06409994735717774, 2.9, 0.009999895095825195, -0.0) on 55 1049312984 2021-09-07 09:10:34.010157 id_data : 136 * BIG TIME 1480.0000269412994 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316209 2021-09-07 09:35:23.009898 id_data : 138 * BIG TIME 668.9998891353607 (939.0006575584412, 5, 0, 0, 0.2945285, 0, 0, 0.48689777, 0, 0.20073189, 1.3838999820947646, 0.0138, 0.06409994735717774, 2.9, 0.018999608993530273, -0.6316664799054463) on 56 1049316332 2021-09-07 09:47:53.009987 id_data : 147 * BIG TIME 649.9999890327454 (1086.000019311905, 5, 0, 0, 0, 0, 0, 0.69907516, 0, 0.23055789, 1.6300000094890594, 0.0168, 0.08039999685287476, 2.9, 0.01100021505355835, -0.0) on 68 1049317197 2021-09-07 10:02:34.010134 id_data : 168 * BIG TIME 540.0001981258392 (1189.0002081394196, 0, 0.8074409, 0, 0, 0, 0, 0, 0, 0, 1.7199999867916107, 0.0194, 0.09519999706745148, 2.9, 0.009999808073043823, -0.0) on 78 1049318212 2021-09-07 10:16:24.010117 id_data : 198 * BIG TIME 190.00007104873657 (1199.0000162124634, 5, 0, 0.22708784, 0, 0, 0, 0.7244179, 0, 0, 1.781000003194809, 0.0202, 0.10109996955394746, 2.9, 0.011000241041183472, -0.0) on 79 1049318219 2021-09-07 10:20:04.010153 id_data : 202 **Count Time bigger than 30s : 31 #Number Photos for regression : {'07092021': {2107751280: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 80.99965000152588, 2107751017: 0}, 2107750907: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 49.000049114227295, 2107751017: 0}, 2107750908: {2107751013: 0, 2107751014: 11.000201940536499, 2107751015: 0, 2107751016: 534.9997780323029, 2107751017: 0}, 2107750909: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750910: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 31.000057697296143, 2107751017: 0}, 2107750911: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 564.0013737678528, 2107751017: 19.999656200408936}, 2107750912: {2107751013: 0, 2107751014: 0, 2107751015: 0, 2107751016: 0, 2107751017: 0}, 2107750913: {2107751013: 0, 2107751014: 8.999944925308228, 2107751015: 0, 2107751016: 89.99986672401428, 2107751017: 9.999994993209839}}} 07092021|rungis_aluminium, 05102018_papier_non_papier_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_aluminium, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_dense:80.99965000152588 07092021|rungis_aluminium, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_carton, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_carton, 05102018_papier_non_papier_tres_dense:49.000049114227295 07092021|rungis_carton, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_dense:0 07092021|rungis_papier, 05102018_papier_non_papier_peu_dense:11.000201940536499 07092021|rungis_papier, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_papier, 05102018_papier_non_papier_tres_dense:534.9997780323029 07092021|rungis_papier, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_plastique_clair, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_dense:31.000057697296143 07092021|rungis_plastique_dur, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_dense:564.0013737678528 07092021|rungis_plastique_fonce, 05102018_papier_non_papier_tres_peu_dense:19.999656200408936 07092021|rungis_tapis_vide, 05102018_papier_non_papier_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_peu_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_dense:0 07092021|rungis_tapis_vide, 05102018_papier_non_papier_tres_peu_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_dense:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_peu_dense:8.999944925308228 07092021|rungis_tetrapak, 05102018_papier_non_papier_presque_vide:0 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_dense:89.99986672401428 07092021|rungis_tetrapak, 05102018_papier_non_papier_tres_peu_dense:9.999994993209839 #Number Photos for regression amount gros magasin papier (time_diff then nb_photo) : We have not displayed the number of photos removed for one material since Rungis_Papier wasn't in the thcl used ! 07092021_time_diff_distrib Number amount portfolio for this type of dechet : aluminium 8 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_aluminium_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341619 with name like 07092021_aluminium_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : carton 6 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_carton_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341620 with name like 07092021_carton_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : papier 29 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341621 with name like 07092021_papier_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_papier_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341622 with name like 07092021_papier_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_clair 0 Number amount portfolio for this type of dechet : plastique_dur 2 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_dur_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341623 with name like 07092021_plastique_dur_05102018_papier_non_papier_tres_dense Number amount portfolio for this type of dechet : plastique_fonce 42 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341624 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341625 with name like 07092021_plastique_fonce_05102018_papier_non_papier_tres_peu_dense Number amount portfolio for this type of dechet : tapis_vide 0 Number amount portfolio for this type of dechet : tetrapak 11 https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341626 with name like 07092021_tetrapak_05102018_papier_non_papier_peu_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341627 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_dense https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense&access_token=0fc1cdda0f63f39f777d9cb33b1aa204 Created to study and clean : 22341628 with name like 07092021_tetrapak_05102018_papier_non_papier_tres_peu_dense NUMBER BATCH : 15 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['pcnc', 'pcm', 'jrm', 'flux_dev', 'pehd_pp', 'papier', 'carton', 'plastique_dur', 'plastique_clair', 'pet_clair', 'plastique_fonce', 'tetrapak', 'aluminium', 'carton_emr', 'grands_cartons', 'gros_de_magasin', 'tapis_vide'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 191.00026988983154, 3: 0, 4: 0}, 'duration': 9008.999763965607, 'nb_balles_papier': 0.19500026988983155, 'begin_time_port': 'image_07092021_05_20_04_010050m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.19500026988983155 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 25, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 225.99965572357178, 3: 0, 4: 0}, 'duration': 2329.999878883362, 'nb_balles_papier': 0.2299996557235718, 'begin_time_port': 'image_07092021_07_50_23_010046m0.jpg 0.010000231981277466 for time 10.000231981277466, id_amount 3 this amount prod time diff : 0.010000231981277466'} Production hashtag (incorrect ponderation at 20-10-18) : 0.2299996557235718 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 141.00006794929504, 3: 0, 4: 0}, 'duration': 189.9997718334198, 'nb_balles_papier': 0.14100006794929504, 'begin_time_port': 'image_07092021_08_29_24_010122m0.jpg 0.011000197172164917 for time 11.000197172164917, id_amount 3 this amount prod time diff : 0.011000197172164917'} Production hashtag (incorrect ponderation at 20-10-18) : 0.14100006794929504 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 9.000005006790161, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.00900000500679016, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.00900000500679016 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 5, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 148.99991703033447, 3: 0, 4: 0}, 'duration': 698.9998500347137, 'nb_balles_papier': 0.15099991703033447, 'begin_time_port': 'image_07092021_08_40_04_010052m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.15099991703033447 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 1, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 11.000216960906982, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.011000216960906983, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.011000216960906983 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 28.9998140335083, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.0289998140335083, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.0289998140335083 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 11, 3: 0, 4: 0}, 'map_time_amount': {0: 11.000201940536499, 1: 0, 2: 111.00051093101501, 3: 0, 4: 0}, 'duration': 330.0000479221344, 'nb_balles_papier': 0.12300071287155151, 'begin_time_port': 'image_07092021_08_52_53_009918m0.jpg 0.01 for time 10.0, id_amount 3 this amount prod time diff : 0.01'} Production hashtag (incorrect ponderation at 20-10-18) : 0.12300071287155151 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 8, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.99999809265137, 3: 0, 4: 0}, 'duration': 291.0001850128174, 'nb_balles_papier': 0.06299999809265137, 'begin_time_port': 'image_07092021_09_02_43_009964m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.06299999809265137 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 1, 1: 0, 2: 16, 3: 0, 4: 0}, 'map_time_amount': {0: 8.999944925308228, 1: 0, 2: 148.99963188171387, 3: 0, 4: 0}, 'duration': 3119.999976873398, 'nb_balles_papier': 0.16199957680702212, 'begin_time_port': 'image_07092021_09_10_34_010157m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.16199957680702212 We filter photos on hashtag condition ! result_one_balle_Type_aluminium:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 7, 3: 1, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 72.00012874603271, 3: 9.999994993209839, 4: 0}, 'duration': 150.00017404556274, 'nb_balles_papier': 0.08200012373924254, 'begin_time_port': 'image_07092021_10_03_24_009930m0.jpg 0.009999775886535644 for time 9.999775886535645, id_amount 3 this amount prod time diff : 0.009999775886535644'} Production hashtag (incorrect ponderation at 20-10-18) : 0.08200012373924254 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_tetrapak:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 2, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 19.99965810775757, 3: 0, 4: 0}, 'duration': 0, 'nb_balles_papier': 0.01999965810775757, 'begin_time_port': 'image'} Production hashtag (incorrect ponderation at 20-10-18) : 0.01999965810775757 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 10, 3: 2, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 81.00049686431885, 3: 19.999656200408936, 4: 0}, 'duration': 428.99973487854004, 'nb_balles_papier': 0.10300015306472779, 'begin_time_port': 'image_07092021_10_16_24_010117m0.jpg 0.001 for time 1, id_amount 3 this amount prod time diff : 0.001'} Production hashtag (incorrect ponderation at 20-10-18) : 0.10300015306472779 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_papier:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 4, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 40.00009298324585, 3: 0, 4: 0}, 'duration': 38.99979090690613, 'nb_balles_papier': 0.04000009298324585, 'begin_time_port': 'image_07092021_10_23_44_010139m0.jpg 0.011000287055969239 for time 11.000287055969238, id_amount 3 this amount prod time diff : 0.011000287055969239'} Production hashtag (incorrect ponderation at 20-10-18) : 0.04000009298324585 We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! result_one_balle_Type_plastique_fonce:{'day': '07092021', 'map_nb_amount': {0: 0, 1: 0, 2: 6, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 60.00031113624573, 3: 0, 4: 0}, 'duration': 98.99990916252136, 'nb_balles_papier': 0.060000311136245724, 'begin_time_port': 'image_07092021_10_24_34_010156m0.jpg 0.011000226020812989 for time 11.000226020812988, id_amount 3 this amount prod time diff : 0.011000226020812989'} Production hashtag (incorrect ponderation at 20-10-18) : 0.060000311136245724 We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 15 list_same_port_ids : [13545772] find same portfolio which already exist 13545772 , we will use it list_same_port_ids : [13545774] find same portfolio which already exist 13545774 , we will use it list_same_port_ids : [13545777] find same portfolio which already exist 13545777 , we will use it list_same_port_ids : [5570414] find same portfolio which already exist 5570414 , we will use it list_same_port_ids : [13545779] find same portfolio which already exist 13545779 , we will use it list_same_port_ids : [13545780] find same portfolio which already exist 13545780 , we will use it list_same_port_ids : [13545783] find same portfolio which already exist 13545783 , we will use it list_same_port_ids : [13545785] find same portfolio which already exist 13545785 , we will use it list_same_port_ids : [13545787] find same portfolio which already exist 13545787 , we will use it list_same_port_ids : [13545788] find same portfolio which already exist 13545788 , we will use it list_same_port_ids : [13543473] find same portfolio which already exist 13543473 , we will use it list_same_port_ids : [13543474] find same portfolio which already exist 13543474 , we will use it list_same_port_ids : [13543475] find same portfolio which already exist 13543475 , we will use it list_same_port_ids : [13543476] find same portfolio which already exist 13543476 , we will use it list_same_port_ids : [13543477] find same portfolio which already exist 13543477 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545772 To do Qualite : 0.005823220486111111 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545774 AND mptpi.`type`=4199 To do Qualite : 0.1888521086140681 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545777 To do Qualite : 0.007415846836419753 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5570414 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545779 To do Qualite : 0.004572120949074074 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545780 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545783 To do Qualite : 0.00907640496399177 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545785 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545787 To do Qualite : 0.01485129824918373 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13545788 AND mptpi.`type`=4199 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543473 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : step 0 init_dummy_multi_datou is not linked in the step_by_step architecture ! WARNING : step 1294 init_dummy_multi_datou is not linked in the step_by_step architecture ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543474 To do Qualite : 0.003848153410463827 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543475 AND mptpi.`type`=4199 To do Qualite : 0.11478003563407302 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! TODO SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543476 To do Qualite : 0.019897576026366652 # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 10257 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 10261 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 10261 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 10263 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 10264 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10258 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 10260 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 10259 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 10259 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 10306 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 11081 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 10260 doesn't seem to be define in the database( WARNING : type of input 3 of step 10259 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 10260 doesn't seem to be define in the database( WARNING : output 1 of step 10258 have datatype=7 whereas input 1 of step 10260 have datatype=None WARNING : type of output 2 of step 10257 doesn't seem to be define in the database( WARNING : type of input 2 of step 10261 doesn't seem to be define in the database( WARNING : output 0 of step 10257 have datatype=16 whereas input 0 of step 10264 have datatype=1 WARNING : output 1 of step 10257 have datatype=2 whereas input 1 of step 10264 have datatype=7 WARNING : output 0 of step 10263 have datatype=6 whereas input 2 of step 10264 have datatype=5 WARNING : type of output 2 of step 10264 doesn't seem to be define in the database( WARNING : type of input 1 of step 10258 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 10260 have datatype=10 whereas input 3 of step 10306 have datatype=6 DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=13543477 AND mptpi.`type`=4199 To do elapsed_time : count_nb_balles_and_create_portfolio 21.424570083618164 # DISPLAY ALL COLLECTED DATA : {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}} time spend for datou_step_exec : 40.964970111846924 time spend to save output : 1.7404556274414062e-05 total time spend for step 1 : 40.9649875164032 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1049318362, 1049318360, 1049318358, 1049318356, 1049318342, 1049318339, 1049318337, 1049318311, 1049318310, 1049318309, 1049318294, 1049318293, 1049318291, 1049318289, 1049318288, 1049318287, 1049318279, 1049318276, 1049318273, 1049318271, 1049318268, 1049318265, 1049318260, 1049318257, 1049318253, 1049318250, 1049318247, 1049318246, 1049318222, 1049318219, 1049318216, 1049318214, 1049318213, 1049318212, 1049317554, 1049317551, 1049317549, 1049317546, 1049317542, 1049317536, 1049317526, 1049317525, 1049317524, 1049317522, 1049317520, 1049317517, 1049317497, 1049317493, 1049317491, 1049317489, 1049317487, 1049317485, 1049317468, 1049317461, 1049317457, 1049317453, 1049317444, 1049317440, 1049317359, 1049317333, 1049317282, 1049317225, 1049317210, 1049317197, 1049316790, 1049316785, 1049316782, 1049316778, 1049316752, 1049316749, 1049316610, 1049316600, 1049316597, 1049316594, 1049316588, 1049316582, 1049316545, 1049316543, 1049316540, 1049316537, 1049316534, 1049316520, 1049316338, 1049316336, 1049316332, 1049316331, 1049316257, 1049316255, 1049316222, 1049316216, 1049316214, 1049316212, 1049316210, 1049316209, 1049313025, 1049312984, 1049312803, 1049312588, 1049312585, 1049312583, 1049312579, 1049312574, 1049312573, 1049312571, 1049312568, 1049312566, 1049312562, 1049312556, 1049312508, 1049312489, 1049312488, 1049312487, 1049312485, 1049312484, 1049312464, 1049312463, 1049312462, 1049312461, 1049312460, 1049312449, 1049312445, 1049312444, 1049312442, 1049312440, 1049312438, 1049312429, 1049312426, 1049312424, 1049312422, 1049312420, 1049312409, 1049312406, 1049312404, 1049312363, 1049312208, 1049311964, 1049311963, 1049311962, 1049311961, 1049311960, 1049311943, 1049311938, 1049311937, 1049311935, 1049311934, 1049311932, 1049311795, 1049311793, 1049311791, 1049311771, 1049311767, 1049311267, 1049311266, 1049311263, 1049311252, 1049311199, 1049311136, 1049311073, 1049311009, 1049311006, 1049310994, 1049310992, 1049310991, 1049310984, 1049310982, 1049310981, 1049310919, 1049310914, 1049310911, 1049310909, 1049310907, 1049310905, 1049310165, 1049310162, 1049310159, 1049310145, 1049310141, 1049310139, 1049310138, 1049310134, 1049310132, 1049309737, 1049309734, 1049309732, 1049309706, 1049309703, 1049309701, 1049309686, 1049309681, 1049309677, 1049309675, 1049309672, 1049309670, 1049309658, 1049309657, 1049309656, 1049309655, 1049309653, 1049309651, 1049309605, 1049309603, 1049309599, 1049309597, 1049309595, 1049309592, 1049309385, 1049309383, 1049309382, 1049309381, 1049309380, 1049309379, 1049309345, 1049308384, 1049308381, 1049308376, 1049308280, 1049308276, 1049308275, 1049308235, 1049307693, 1049306823, 1049306804, 1049306792, 1049306791, 1049306635, 1049306205, 1049304810, 1049303925, 1049296996, 1049296121, 1049294990, 1049293230] Looping around the photos to save general results len do output : 1 /4599398Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318362', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318360', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318358', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049318356', 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'4599398', '1049310141', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310139', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310138', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310134', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049310132', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309737', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309734', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309732', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309706', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309703', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309701', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309686', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309681', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309677', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309675', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309672', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309670', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309658', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309657', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309656', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309655', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309653', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309651', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309605', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309603', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309599', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309597', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309595', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309592', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309385', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309383', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309382', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309380', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309379', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049309345', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308384', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308381', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308376', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308280', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308276', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308275', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049308235', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049307693', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306823', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306804', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306792', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306791', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306635', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049306205', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049304810', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049303925', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296996', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049296121', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049294990', None, None, None, None, None, None) ('3789', None, None, None, None, None, None, None, None) ('3789', '4599398', '1049293230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 233 time used for this insertion : 0.0515139102935791 save_final save missing photos in datou_result : After save, about to update current ! Result test rubbia : {'4599398': ([[0, 7, 8, 9, 11, 12, 13, 14, 17, 18], [19, 20, 22, 23, 25, 26, 27, 38, 39, 40, 51, 52, 53, 55, 60, 61, 62, 63, 71, 76, 77, 81, 82, 83, 84], [85, 86, 91, 92], [93], [97, 98, 99, 100, 101], [102], [103, 104], [107, 108, 109, 110, 112, 113, 116, 117, 118, 121, 122, 123], [124, 126, 128, 129, 130, 131, 134, 135], [136, 137, 138, 147, 148, 149, 150, 151, 152, 153, 154, 157, 158, 161, 162, 167, 168], [173, 176, 177, 178, 182, 183, 187, 188], [189, 193], [198, 201, 202, 207, 208, 209, 210, 211, 212, 213, 214, 215], [216, 217, 218, 220], [221, 224, 225, 226, 227, 231]], {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, {13545772: {'list_of_photos': [1049293230, 1049306791, 1049294990, 1049306792, 1049306823, 1049307693, 1049308235, 1049308275, 1049308376, 1049308381], 'hashtag': 'papier'}, 13545774: {'list_of_photos': [1049308384, 1049309345, 1049309380, 1049309381, 1049309383, 1049309385, 1049309592, 1049309658, 1049309670, 1049309672, 1049310132, 1049310134, 1049310138, 1049310141, 1049310905, 1049310907, 1049310909, 1049310911, 1049310994, 1049311199, 1049311252, 1049311767, 1049311771, 1049311791, 1049311793], 'hashtag': 'plastique_fonce'}, 13545777: {'list_of_photos': [1049311795, 1049311932, 1049311943, 1049311960], 'hashtag': 'papier'}, 5570414: {'list_of_photos': [1049311961], 'hashtag': 'plastique_fonce'}, 13545779: {'list_of_photos': [1049312208, 1049312363, 1049312404, 1049312406, 1049312409], 'hashtag': 'papier'}, 13545780: {'list_of_photos': [1049312420], 'hashtag': 'plastique_fonce'}, 13545783: {'list_of_photos': [1049312422, 1049312424], 'hashtag': 'papier'}, 13545785: {'list_of_photos': [1049312438, 1049312440, 1049312442, 1049312444, 1049312449, 1049312460, 1049312463, 1049312464, 1049312484, 1049312488, 1049312489, 1049312508], 'hashtag': 'plastique_fonce'}, 13545787: {'list_of_photos': [1049312556, 1049312566, 1049312571, 1049312573, 1049312574, 1049312579, 1049312588, 1049312803], 'hashtag': 'papier'}, 13545788: {'list_of_photos': [1049312984, 1049313025, 1049316209, 1049316332, 1049316336, 1049316338, 1049316520, 1049316534, 1049316537, 1049316540, 1049316543, 1049316588, 1049316594, 1049316610, 1049316749, 1049316790, 1049317197], 'hashtag': 'plastique_fonce'}, 13543473: {'list_of_photos': [1049317359, 1049317453, 1049317457, 1049317461, 1049317489, 1049317491, 1049317520, 1049317522], 'hashtag': 'aluminium'}, 13543474: {'list_of_photos': [1049317524, 1049317542], 'hashtag': 'tetrapak'}, 13543475: {'list_of_photos': [1049318212, 1049318216, 1049318219, 1049318253, 1049318257, 1049318260, 1049318265, 1049318268, 1049318271, 1049318273, 1049318276, 1049318279], 'hashtag': 'plastique_fonce'}, 13543476: {'list_of_photos': [1049318287, 1049318288, 1049318289, 1049318293], 'hashtag': 'papier'}, 13543477: {'list_of_photos': [1049318294, 1049318311, 1049318337, 1049318339, 1049318342, 1049318362], 'hashtag': 'plastique_fonce'}}, {2107751280: 8, 2107750907: 0, 2107750908: 33, 2107750909: 0, 2107750910: 0, 2107750911: 74, 2107750912: 0, 2107750913: 2}, {'amount_uploaded_and_tagged': {'07092021': {'nb_upload': 232, 'nb_taggue_class': 232, 'nb_taggue_densite': 232, 'nb_descriptors': 232, 'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}}, 'map_all_result_after_group_moy_exp': {'number_port': 15, 'count_photo_in_port': 117, 'nb_port_per_class': {'rungis_aluminium': {'nb_photos': 8, 'nb_portfolios': 1}, 'rungis_carton': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_papier': {'nb_photos': 33, 'nb_portfolios': 6}, 'rungis_plastique_clair': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_dur': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_plastique_fonce': {'nb_photos': 74, 'nb_portfolios': 7}, 'rungis_tapis_vide': {'nb_photos': 0, 'nb_portfolios': 0}, 'rungis_tetrapak': {'nb_photos': 2, 'nb_portfolios': 1}}}, 'map_info_after_moyenne_mobile': {'07092021': {'distrib_time_diff': {'nb': 207, 'mean': 12.512076641626404, 'stddev': 12.296218880583977, 'min': 0.0, 'max': 119.00011491775513, 'quantil_10': {'min': [8.999778032302856], 'max': [11.000291109085083]}, 'quantil_100': {'min': [8.999709129333496], 'max': [61.00024700164795]}, 'quantil_1000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_5000': {'min': [0.0], 'max': [119.00011491775513]}, 'quantil_10000': {'min': [0.0], 'max': [119.00011491775513]}}, 'time_diff': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 80.99965000152588, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 49.000049114227295, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000201940536499, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 534.9997780323029, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 31.000057697296143, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 564.0013737678528, '05102018_papier_non_papier_tres_peu_dense': 19.999656200408936}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 8.999944925308228, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 89.99986672401428, '05102018_papier_non_papier_tres_peu_dense': 9.999994993209839}}, 'time_diff_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 11.000208854675293, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 158.99993062019348, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 101.00013995170593, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 18.999826192855835, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 168.99926328659058, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 21.00012707710266, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 489.9993727207184, '05102018_papier_non_papier_tres_peu_dense': 11.000169038772583}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 38.99988508224487, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 170.0003685951233, '05102018_papier_non_papier_tres_peu_dense': 0}}, 'nb_photos': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 8, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 6, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 28, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 2, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 40, '05102018_papier_non_papier_tres_peu_dense': 2}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 9, '05102018_papier_non_papier_tres_peu_dense': 1}}, 'nb_photos_removed': {'rungis_aluminium': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_carton': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 5, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_papier': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 2, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 16, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_clair': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_dur': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_plastique_fonce': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 1, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 46, '05102018_papier_non_papier_tres_peu_dense': 1}, 'rungis_tapis_vide': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 0, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 0, '05102018_papier_non_papier_tres_peu_dense': 0}, 'rungis_tetrapak': {'05102018_papier_non_papier_dense': 0, '05102018_papier_non_papier_peu_dense': 4, '05102018_papier_non_papier_presque_vide': 0, '05102018_papier_non_papier_tres_dense': 17, '05102018_papier_non_papier_tres_peu_dense': 0}}}}, 'map_amount_per_hashtag': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}, 'count': {'rungis_aluminium': [(10, 11)], 'rungis_carton': [], 'rungis_papier': [(0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (13, 14)], 'rungis_plastique_clair': [], 'rungis_plastique_dur': [], 'rungis_plastique_fonce': [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10), (12, 13), (14, 15)], 'rungis_tapis_vide': [], 'rungis_tetrapak': [(11, 12)]}})}| Result context_with_local_rubbia.cache_model_config.map_io test rubbia : {'input': {}, 'output': {}}| ############################### TEST rubbia_split_dark ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score_with_photo list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFBFFBFBFBFBFFFFBFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 64 ; length of list_pids : 64 ; length of list_args : 64 time to download the photos : 4.637606620788574 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score_with_photo Wed Apr 23 11:26:23 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec ----- Debut du copier-coller des param necessaire pour fonction main de STS ----- TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('18', 4), ('19', 5), ('20', 5), ('24', 8), ('26', 6), ('17', 1), ('27', 9), ('51', 7), ('28', 2), ('21', 4), ('52', 2), ('25', 6), ('50', 3), ('22', 2)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 06102021 4608689 Nombre de photos uploadées : 64 / 23040 (0%) 06102021 4608689 Nombre de photos taguées (types de déchets): 0 / 64 (0%) 06102021 4608689 Nombre de photos taguées (volume) : 0 / 64 (0%) elapsed_time : load_data_split_time_score 1.3828277587890625e-05 elapsed_time : order_list_meta_photo_and_scores 1.4543533325195312e-05 ???????????????????????????????????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.10792422294616699 elapsed_time : insert_dashboard_record_day_entry 0.02896404266357422 ***** BEGIN SPLIT BY DARK ***** To DO 08/10/21 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 1 time used for this insertion : 0.008367300033569336 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 2 time used for this insertion : 0.008945226669311523 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 3 time used for this insertion : 0.008949041366577148 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 4 time used for this insertion : 0.008694887161254883 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 5 time used for this insertion : 0.008188009262084961 elapsed_time : SPLIT_BY_DARK 0.051430463790893555 ***** END SPLIT BY DARK ***** ((1055001085,), (1055008638,), (1055010730,), (1055011086,), (1055012686,)) ***** BEGIN SPLIT TIME ***** [12, 20, 29, 38, 51] ````````````````````````````````````````````````````````````````list printed: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.008351802825927734 ***** END SPLIT TIME ***** NUMBER BATCH : 7 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['refus', 'jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 172.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_101733.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 72.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102049.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 52.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102404.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 53.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102525.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 81.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_102645.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 Empty batch, bug or could have been filtered ! ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '06102021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 102.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20211006_145039.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 6 list_same_port_ids : [4938484] find same portfolio which already exist 4938484 , we will use it list_same_port_ids : [4938485] find same portfolio which already exist 4938485 , we will use it list_same_port_ids : [4938486] find same portfolio which already exist 4938486 , we will use it list_same_port_ids : [4938487] find same portfolio which already exist 4938487 , we will use it list_same_port_ids : [4938488] find same portfolio which already exist 4938488 , we will use it list_same_port_ids : [4756245] find same portfolio which already exist 4756245 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938484 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938485 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938486 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938487 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4938488 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4756245 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 1.006943702697754 # DISPLAY ALL COLLECTED DATA : {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} ------ Fin du Copier-Coller ------ ---------- ONE RESULT --------- ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) ---------- END de ONE RESULT ---------- Suppression des photos Telecharges time spend for datou_step_exec : 7.524064302444458 time spend to save output : 0.00010943412780761719 total time spend for step 1 : 7.524173736572266 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score_with_photo we use saveGeneral [1055011086, 1055011076, 1055011074, 1055011072, 1055010743, 1055010739, 1055010737, 1055010730, 1055010725, 1055010723, 1055010143, 1055008638, 1055008599, 1055003131, 1055002045, 1055001545, 1055001542, 1055001092, 1055001085, 1055000228, 1055000070, 1055000068, 1055000063, 1055000059, 1055000055, 1055013727, 1055013724, 1055013693, 1055012727, 1055012722, 1055012686, 1055012684, 1055011740, 1055011733, 1055011726, 1055011459, 1055011454, 1055011441, 1055003357, 1055003348, 1055003292, 1055003278, 1055003266, 1055003261, 1055003259, 1055003249, 1055003202, 1055003198, 1055003197, 1055003185, 1055003134, 1055008597, 1055008184, 1055008181, 1055007992, 1055007953, 1055007950, 1055004798, 1055004627, 1055004608, 1055004600, 1055004278, 1055004217, 1055003679] Looping around the photos to save general results len do output : 5 /[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]] /{'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]} /{4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}} /{2107757407: 59} /{'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}} before output type Managing all output in save final without adding information in the mtr_datou_result ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011086', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011076', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011074', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011072', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010743', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010739', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010737', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010730', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010725', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010723', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055010143', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008638', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008599', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003131', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055002045', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001545', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001542', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001092', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055001085', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000228', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000070', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000068', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000063', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000059', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055000055', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013724', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055013693', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012727', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012722', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012686', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055012684', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011740', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011733', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011726', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011459', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011454', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055011441', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003357', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003348', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003292', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003266', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003261', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003259', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003249', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003202', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003198', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003197', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003185', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003134', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008597', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008184', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055008181', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007992', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007953', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055007950', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004798', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004627', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004608', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004600', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004278', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055004217', None, None, None, None, None, None) ('3787', None, None, None, None, None, None, None, None) ('3787', '4608689', '1055003679', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 64 time used for this insertion : 0.0221250057220459 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}}) Result test split dark : ([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [13, 14, 15, 16, 17, 18, 19], [21, 22, 23, 24, 25, 26, 27, 28], [30, 31, 32, 33, 34, 35, 36, 37], [39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], [], [52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]], {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, {4938484: {'list_of_photos': [1055000228, 1055000055, 1055003357, 1055007950, 1055003348, 1055007953, 1055000059, 1055007992, 1055008181, 1055003197, 1055003198, 1055008184], 'hashtag': 'jrm'}, 4938485: {'list_of_photos': [1055000063, 1055004600, 1055008597, 1055003134, 1055008599, 1055003679, 1055004627], 'hashtag': 'jrm'}, 4938486: {'list_of_photos': [1055004217, 1055010143, 1055004278, 1055010723, 1055003131, 1055003202, 1055010725, 1055000068], 'hashtag': 'jrm'}, 4938487: {'list_of_photos': [1055010737, 1055010739, 1055003278, 1055010743, 1055011072, 1055011074, 1055011076, 1055000070], 'hashtag': 'jrm'}, 4938488: {'list_of_photos': [1055011441, 1055011454, 1055003185, 1055011459, 1055001092, 1055001542, 1055003292, 1055011726, 1055011733, 1055011740, 1055012684, 1055002045], 'hashtag': 'jrm'}, 4756245: {'list_of_photos': [1055012722, 1055004798, 1055004608, 1055012727, 1055013693, 1055013724, 1055003249, 1055001545, 1055003259, 1055013727, 1055003266, 1055003261], 'hashtag': 'jrm'}}, {2107757407: 59}, {'amount_uploaded_and_tagged': {'06102021': {'nb_upload': 64, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6)]}})| ############################### TEST rubbia_append ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.024243831634521484 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Wed Apr 23 11:26:31 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('30', 1), ('36', 1), ('43', 1)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4599006 Nombre de photos uploadées : 3 / 23040 (0%) 21092021 4599006 Nombre de photos taguées (types de déchets): 0 / 3 (0%) 21092021 4599006 Nombre de photos taguées (volume) : 0 / 3 (0%) elapsed_time : load_data_split_time_score 3.0994415283203125e-06 elapsed_time : order_list_meta_photo_and_scores 9.5367431640625e-06 ??? elapsed_time : fill_and_build_computed_from_old_data 0.0005505084991455078 elapsed_time : insert_dashboard_record_day_entry 0.02411365509033203 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 3, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('12', 1), ('-0', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 21092021 4505992 Nombre de photos uploadées : 1 / 23040 (0%) 21092021 4505992 Nombre de photos taguées (types de déchets): 0 / 1 (0%) 21092021 4505992 Nombre de photos taguées (volume) : 0 / 1 (0%) elapsed_time : load_data_split_time_score 3.0994415283203125e-06 elapsed_time : order_list_meta_photo_and_scores 8.58306884765625e-06 ? elapsed_time : fill_and_build_computed_from_old_data 0.0002319812774658203 elapsed_time : insert_dashboard_record_day_entry 0.02295398712158203 ---------- APPEND TASK BEGIN ---------- ---------- APPEND TASK END ---------- We will return after consolidate but for now we need the day, how to get it, for now depending on the previous heavy steps find url: select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4599006 order by id desc limit 1 find url: https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4505992_21-09-2021.pdf select completion_json, dashboard_run_id from MTRPhoto.dashboard_results where mtr_portfolio_id = 4505992 order by id desc limit 1 NUMBER BATCH : 0 # DISPLAY ALL COLLECTED DATA : {'21092021': {'nb_upload': 1, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 2.7140276432037354 time spend to save output : 3.814697265625e-05 total time spend for step 1 : 2.7140657901763916 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1054572537, 1054572534, 1054572532, 1051605195] Looping around the photos to save general results len do output : 2 /4599006Didn't retrieve data . /4505992Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572537', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572534', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4599006', '1054572532', None, None, None, None, None, None) ('3856', None, None, None, None, None, None, None, None) ('3856', '4505992', '1051605195', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.013491630554199219 save_final save missing photos in datou_result : After save, about to update current ! ############################### TEST rubbia_horaire ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.015018939971923828 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Wed Apr 23 11:26:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('02', 6), ('05', 8), ('06', 19)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 08032021 3609515 Nombre de photos uploadées : 33 / 23040 (0%) 08032021 3609515 Nombre de photos taguées (types de déchets): 0 / 33 (0%) 08032021 3609515 Nombre de photos taguées (volume) : 0 / 33 (0%) elapsed_time : load_data_split_time_score 4.291534423828125e-06 elapsed_time : order_list_meta_photo_and_scores 1.71661376953125e-05 ????????????????????????????????? elapsed_time : fill_and_build_computed_from_old_data 0.0025730133056640625 elapsed_time : insert_dashboard_record_day_entry 0.02233600616455078 Creating list_photo_total elapsed_time : select_descriptors 0.011093378067016602 08032021 3609515 Nombre de photos avec descriptors (type 3963) : 0 / 33 (0%) Missing descriptors for photos 0 and 1014054233 0:00:00|ON:Missing descriptors for photos 1014054233 and 1014054232 Missing descriptors for photos 1014054232 and 1014054231 Missing descriptors for photos 1014054231 and 1014054230 Missing descriptors for photos 1014054230 and 1014054235 Missing descriptors for photos 1014054235 and 1014054234 Missing descriptors for photos 1014054234 and 1014097492 Missing descriptors for photos 1014097492 and 1014097499 Missing descriptors for photos 1014097499 and 1014097497 Missing descriptors for photos 1014097497 and 1014097580 Missing descriptors for photos 1014097580 and 1014097924 Missing descriptors for photos 1014097924 and 1014098236 Missing descriptors for photos 1014098236 and 1014098602 Missing descriptors for photos 1014098602 and 1014099035 Missing descriptors for photos 1014099035 and 1014105778 Missing descriptors for photos 1014105778 and 1014105777 Missing descriptors for photos 1014105777 and 1014105784 Missing descriptors for photos 1014105784 and 1014105783 Missing descriptors for photos 1014105783 and 1014105782 Missing descriptors for photos 1014105782 and 1014105781 Missing descriptors for photos 1014105781 and 1014105786 Missing descriptors for photos 1014105786 and 1014105785 Missing descriptors for photos 1014105785 and 1014105791 Missing descriptors for photos 1014105791 and 1014105790 Missing descriptors for photos 1014105790 and 1014105798 Missing descriptors for photos 1014105798 and 1014105797 Missing descriptors for photos 1014105797 and 1014105796 Missing descriptors for photos 1014105796 and 1014105795 Missing descriptors for photos 1014105795 and 1014105800 Missing descriptors for photos 1014105800 and 1014105799 Missing descriptors for photos 1014105799 and 1014106095 Missing descriptors for photos 1014106095 and 1014106094 Missing descriptors for photos 1014106094 and 1014106093 08032021 Removing 0 photos because of the 'same image' condition Total on : 0 Total off : 0.0 list_time_off Warning in study_and_display_distrib_list : min=max : 0.0 0.0 dist_desc Warning in study_and_display_distrib_list : min=max : -1 -1 begin to insert list_values into photo_hahstag_ids : length of list_valuse in save_photo_hashtag_id_type : 33 time used for this insertion : 0.014883995056152344 photos_removed : len 0 elapsed_time : remove_photo_duplicate 0.04400801658630371 To do, maybe not at the correct place ! .................................force hashtag to JRM elapsed_time : CREATE_PORT_BATCH_BY_HOUR 0.006960868835449219 NUMBER BATCH : 3 list_ponderation used : [1e-05, 1e-05, 1e-05, 1e-05, 1e-05] , list_hashtag_class_create_as_list : ['jrm'] We filter photos on hashtag condition ! We filter photos on hashtag condition ! We filter photos on hashtag condition ! We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 0 elapsed_time : count_nb_balles_and_create_portfolio 0.023943424224853516 # DISPLAY ALL COLLECTED DATA : {'08032021': {'nb_upload': 33, 'nb_taggue_class': 0, 'nb_taggue_densite': 0, 'nb_descriptors': 0}} time spend for datou_step_exec : 0.17287397384643555 time spend to save output : 3.814697265625e-05 total time spend for step 1 : 0.1729121208190918 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1014106095, 1014106094, 1014106093, 1014105800, 1014105799, 1014105798, 1014105797, 1014105796, 1014105795, 1014105791, 1014105790, 1014105786, 1014105785, 1014105784, 1014105783, 1014105782, 1014105781, 1014105778, 1014105777, 1014099035, 1014098602, 1014098236, 1014097924, 1014097580, 1014097499, 1014097497, 1014097492, 1014054235, 1014054234, 1014054233, 1014054232, 1014054231, 1014054230] Looping around the photos to save general results len do output : 1 /3609515Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106095', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106094', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014106093', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105800', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105799', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105798', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105797', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105796', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105795', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105791', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105790', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105786', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105785', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105784', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105783', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105782', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105781', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105778', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014105777', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014099035', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098602', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014098236', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097924', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097580', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097499', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097497', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014097492', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054235', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054234', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054233', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054232', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054231', None, None, None, None, None, None) ('3181', None, None, None, None, None, None, None, None) ('3181', '3609515', '1014054230', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 34 time used for this insertion : 0.0220487117767334 save_final save missing photos in datou_result : After save, about to update current ! got : {'Rungis_JRM': []} expected : {'Rungis_JRM': [(0, 1), (1, 2), (2, 3)]} ERROR rubbia_horaire FAILED ############################### TEST rle_unique_nms_with_priority ################################ t Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.1486806869506836 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Wed Apr 23 11:26:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 2804 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 10 nb_hashtags : 2 time to prepare the origin masks : 0.673884391784668 time for calcul the mask position with numpy : 0.004278659820556641 nb_pixel_total : 217207 time to create 1 rle with new method : 0.038068294525146484 time for calcul the mask position with numpy : 0.003061056137084961 nb_pixel_total : 1008 time to create 1 rle with old method : 0.002440214157104492 time for calcul the mask position with numpy : 0.003378152847290039 nb_pixel_total : 751 time to create 1 rle with old method : 0.0029349327087402344 time for calcul the mask position with numpy : 0.0033392906188964844 nb_pixel_total : 722 time to create 1 rle with old method : 0.0026619434356689453 time for calcul the mask position with numpy : 0.003627777099609375 nb_pixel_total : 2949 time to create 1 rle with old method : 0.00823664665222168 time for calcul the mask position with numpy : 0.003586292266845703 nb_pixel_total : 497 time to create 1 rle with old method : 0.0014257431030273438 time for calcul the mask position with numpy : 0.003438234329223633 nb_pixel_total : 1086 time to create 1 rle with old method : 0.01664876937866211 time for calcul the mask position with numpy : 0.007473945617675781 nb_pixel_total : 1924 time to create 1 rle with old method : 0.005011796951293945 time for calcul the mask position with numpy : 0.003355741500854492 nb_pixel_total : 413 time to create 1 rle with old method : 0.0013682842254638672 time for calcul the mask position with numpy : 0.0034008026123046875 nb_pixel_total : 526 time to create 1 rle with old method : 0.0014247894287109375 create new chi : 0.11969971656799316 time to delete rle : 0.016216039657592773 batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 1674 TO DO : save crop sub photo not yet done ! save time : 0.16181397438049316 map_output_result : {998957128: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 1.0896713733673096 time spend to save output : 0.00015354156494140625 total time spend for step 1 : 1.089824914932251 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {998957128: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3325502872467041 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Wed Apr 23 11:26:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 10 chid ids of type : 4169 seulement à utiliser dans la step consolidation batch 1 Loaded 10 chid ids of type : 2805 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.028335094451904297 map_output_result : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} End step rle-unique-nms time spend for datou_step_exec : 0.374248743057251 time spend to save output : 8.130073547363281e-05 total time spend for step 1 : 0.3743300437927246 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1066511071: (0.0, 'Should be the crop_list due to order', 0)} Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rle_unique_nms_with_priority list_input_json : [] origin BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.18888545036315918 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rle_unique_nms_with_priority Wed Apr 23 11:26:36 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step rle-unique-nms batch 1 Loaded 91 chid ids of type : 2596 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 43 nb_hashtags : 2 time to prepare the origin masks : 27.40830659866333 time for calcul the mask position with numpy : 0.21379423141479492 nb_pixel_total : 5233657 time to create 1 rle with new method : 0.44164586067199707 time for calcul the mask position with numpy : 0.03358745574951172 nb_pixel_total : 11972 time to create 1 rle with old method : 0.026577472686767578 time for calcul the mask position with numpy : 0.03309822082519531 nb_pixel_total : 15054 time to create 1 rle with old method : 0.03397345542907715 time for calcul the mask position with numpy : 0.03233814239501953 nb_pixel_total : 13954 time to create 1 rle with old method : 0.03159070014953613 time for calcul the mask position with numpy : 0.038559913635253906 nb_pixel_total : 4888 time to create 1 rle with old method : 0.011743783950805664 time for calcul the mask position with numpy : 0.053621530532836914 nb_pixel_total : 1188492 time to create 1 rle with new method : 0.4275095462799072 time for calcul the mask position with numpy : 0.03711295127868652 nb_pixel_total : 184585 time to create 1 rle with new method : 0.6287853717803955 time for calcul the mask position with numpy : 0.03410649299621582 nb_pixel_total : 18620 time to create 1 rle with old method : 0.052136898040771484 time for calcul the mask position with numpy : 0.0359339714050293 nb_pixel_total : 62945 time to create 1 rle with old method : 0.16444826126098633 time for calcul the mask position with numpy : 0.03593707084655762 nb_pixel_total : 9427 time to create 1 rle with old method : 0.02445244789123535 time for calcul the mask position with numpy : 0.04216933250427246 nb_pixel_total : 9081 time to create 1 rle with old method : 0.026300907135009766 time for calcul the mask position with numpy : 0.03468632698059082 nb_pixel_total : 15987 time to create 1 rle with old method : 0.039107561111450195 time for calcul the mask position with numpy : 0.037648677825927734 nb_pixel_total : 33276 time to create 1 rle with old method : 0.08555817604064941 time for calcul the mask position with numpy : 0.035424232482910156 nb_pixel_total : 17533 time to create 1 rle with old method : 0.04417824745178223 time for calcul the mask position with numpy : 0.03523659706115723 nb_pixel_total : 4876 time to create 1 rle with old method : 0.012327432632446289 time for calcul the mask position with numpy : 0.035684823989868164 nb_pixel_total : 25226 time to create 1 rle with old method : 0.05783843994140625 time for calcul the mask position with numpy : 0.035942792892456055 nb_pixel_total : 30773 time to create 1 rle with old method : 0.07182598114013672 time for calcul the mask position with numpy : 0.0347752571105957 nb_pixel_total : 65671 time to create 1 rle with old method : 0.16568994522094727 time for calcul the mask position with numpy : 0.03755974769592285 nb_pixel_total : 12230 time to create 1 rle with old method : 0.042654991149902344 time for calcul the mask position with numpy : 0.03789520263671875 nb_pixel_total : 29560 time to create 1 rle with old method : 0.07602572441101074 time for calcul the mask position with numpy : 0.03656268119812012 nb_pixel_total : 14310 time to create 1 rle with old method : 0.038700103759765625 time for calcul the mask position with numpy : 0.03675365447998047 nb_pixel_total : 15117 time to create 1 rle with old method : 0.04172086715698242 time for calcul the mask position with numpy : 0.04606747627258301 nb_pixel_total : 301487 time to create 1 rle with new method : 0.4061110019683838 time for calcul the mask position with numpy : 0.03539252281188965 nb_pixel_total : 29821 time to create 1 rle with old method : 0.075653076171875 time for calcul the mask position with numpy : 0.04163622856140137 nb_pixel_total : 40299 time to create 1 rle with old method : 0.10146021842956543 time for calcul the mask position with numpy : 0.03556418418884277 nb_pixel_total : 12680 time to create 1 rle with old method : 0.034108638763427734 time for calcul the mask position with numpy : 0.035898447036743164 nb_pixel_total : 9449 time to create 1 rle with old method : 0.02148914337158203 time for calcul the mask position with numpy : 0.03240680694580078 nb_pixel_total : 15168 time to create 1 rle with old method : 0.036766767501831055 time for calcul the mask position with numpy : 0.04073905944824219 nb_pixel_total : 11140 time to create 1 rle with old method : 0.030738353729248047 time for calcul the mask position with numpy : 0.03459310531616211 nb_pixel_total : 29065 time to create 1 rle with old method : 0.07285737991333008 time for calcul the mask position with numpy : 0.034861087799072266 nb_pixel_total : 22774 time to create 1 rle with old method : 0.052849769592285156 time for calcul the mask position with numpy : 0.03714561462402344 nb_pixel_total : 13880 time to create 1 rle with old method : 0.03181624412536621 time for calcul the mask position with numpy : 0.03714919090270996 nb_pixel_total : 155366 time to create 1 rle with new method : 0.7021467685699463 time for calcul the mask position with numpy : 0.035807132720947266 nb_pixel_total : 63941 time to create 1 rle with old method : 0.15627121925354004 time for calcul the mask position with numpy : 0.039369821548461914 nb_pixel_total : 7836 time to create 1 rle with old method : 0.02277970314025879 time for calcul the mask position with numpy : 0.03365468978881836 nb_pixel_total : 7460 time to create 1 rle with old method : 0.01704096794128418 time for calcul the mask position with numpy : 0.03362894058227539 nb_pixel_total : 44600 time to create 1 rle with old method : 0.10758805274963379 time for calcul the mask position with numpy : 0.03333449363708496 nb_pixel_total : 11879 time to create 1 rle with old method : 0.026902437210083008 time for calcul the mask position with numpy : 0.042391300201416016 nb_pixel_total : 44195 time to create 1 rle with old method : 0.10247039794921875 time for calcul the mask position with numpy : 0.03603959083557129 nb_pixel_total : 23652 time to create 1 rle with old method : 0.05971169471740723 time for calcul the mask position with numpy : 0.037836551666259766 nb_pixel_total : 30006 time to create 1 rle with old method : 0.07127642631530762 time for calcul the mask position with numpy : 0.038202762603759766 nb_pixel_total : 15880 time to create 1 rle with old method : 0.05169034004211426 time for calcul the mask position with numpy : 0.0439913272857666 nb_pixel_total : 29845 time to create 1 rle with old method : 0.06907486915588379 time for calcul the mask position with numpy : 0.03518509864807129 nb_pixel_total : 144263 time to create 1 rle with old method : 0.337522029876709 create new chi : 7.084381580352783 time to delete rle : 0.5752518177032471 batch 1 Loaded 44 chid ids of type : 2805 Number RLEs to save : 27884 TO DO : save crop sub photo not yet done ! save time : 1.916152000427246 map_output_result : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} End step rle-unique-nms time spend for datou_step_exec : 37.272738456726074 time spend to save output : 0.021619319915771484 total time spend for step 1 : 37.294357776641846 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {996751167: (1.0, 'Should be the crop_list due to order', 1.0)} batch 1 Loaded 54 chid ids of type : 2805 ############################### TEST random_deformation ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : random_deformation list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.21482515335083008 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:random_deformation Wed Apr 23 11:27:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step random deformation get user info for portfolio 3288640 About to upload 4 photos upload in portfolio : 3287159 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400438_1620143 we have uploaded 4 photos in the portfolio 3287159 time of upload the photos Elapsed time : 2.533712148666382 time spend for datou_step_exec : 5.843759775161743 time spend to save output : 5.4836273193359375e-05 total time spend for step 1 : 5.8438146114349365 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : random_deformation we use saveGeneral [1006293201] Looping around the photos to save general results len do output : 4 /1353003010Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353003011Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353003012Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1353003013Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('2896', None, None, None, None, None, None, None, None) ('2896', '3288640', '1006293201', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 13 time used for this insertion : 0.011993646621704102 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1353003010: ['1006293201', 'temp/1006293201_random_deformation_0.png', []], 1353003011: ['1006293201', 'temp/1006293201_random_deformation_1.png', []], 1353003012: ['1006293201', 'temp/1006293201_random_deformation_2.png', []], 1353003013: ['1006293201', 'temp/1006293201_random_deformation_3.png', []]} name 'urllib' is not defined can't unload the photo : 1006293201 t ############################### TEST tile ################################ test tile avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : tile list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3056826591491699 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:tile Wed Apr 23 11:27:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec verbose : False param_json : {'ETA': 3600, 'new_width': 640, 'new_height': 640, 'token': '5d93a4b2b749464f208c339a1324b78f', 'stride': 0, 'stride_relative': 0, 'portfolio_name': 'results_test_tile', 'crop_hashtag_type_tiled': 3243, 'crop_hashtag_type': 3242, 'arg_aux_upload': {'type_upload': 'python'}, 'host': 'www.fotonower.com'} type(crop_hashtag_type) : type(crop_hashtag_type_tiled) : We consider crop_hashtag_type is an integer ! map_chi_type_to_chi_type_cropped : {3242: 3243} TO DEPRECATE VR 14-6-18 map_filenames : {1008283903: 'temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg'} list_pids : 1 list_pids : 2 list_subpids to replace list_pids : 0 batch 1 Loaded 2 chid ids of type : 3242 ++https://marlene.fotonower.com/api/v1/secured/portfolio/new?name=results_test_tile&access_token=5d93a4b2b749464f208c339a1324b78f created feed_id_new_photos : 22341655 with name results_test_tile feed_id_new_photos : 22341655 filename : temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg photo_id : 1008283903 height_image_input : 2464 width_image_input : 3280 new_width : 640 new_height : 640 stride : 0 stride_relative : 0 chi to copy from the main photo to the tiled photo input_chi_for_this_image_as_chi : 2 list_bib_to_crops : 24 [(0, 640, 0, 640, 0), (0, 640, 640, 1280, 1), (0, 640, 1280, 1920, 2), (0, 640, 1824, 2464, 3), (640, 1280, 0, 640, 4), (640, 1280, 640, 1280, 5), (640, 1280, 1280, 1920, 6), (640, 1280, 1824, 2464, 7), (1280, 1920, 0, 640, 8), (1280, 1920, 640, 1280, 9), (1280, 1920, 1280, 1920, 10), (1280, 1920, 1824, 2464, 11), (1920, 2560, 0, 640, 12), (1920, 2560, 640, 1280, 13), (1920, 2560, 1280, 1920, 14), (1920, 2560, 1824, 2464, 15), (2560, 3200, 0, 640, 16), (2560, 3200, 640, 1280, 17), (2560, 3200, 1280, 1920, 18), (2560, 3200, 1824, 2464, 19), (2640, 3280, 0, 640, 20), (2640, 3280, 640, 1280, 21), (2640, 3280, 1280, 1920, 22), (2640, 3280, 1824, 2464, 23)] new_crops_tiles : 24 crop_transformed : 7 batch 1 Loaded 24 chid ids of type : 17 treat the image : temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53.jpg , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 before upload mediasElapsed time : 0.41695284843444824 on upload les photos avec python init cache_photo without model_param we have 24 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400448_1620143 we have uploaded 24 photos in the portfolio 22341655 Importing ! upload mediasElapsed time : 16.94630765914917 , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23Saving 7 CHIs. batch 1 Loaded 7 chid ids of type : 3243 Number RLEs to save : 2937 TO DO : save crop sub photo not yet done ! end of tileElapsed time : 17.21515727043152 time spend for datou_step_exec : 24.069029569625854 time spend to save output : 3.361701965332031e-05 total time spend for step 1 : 24.069063186645508 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {'1353003033': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_0.jpg'], '1353003034': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_1.jpg'], '1353003035': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_2.jpg'], '1353003036': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_3.jpg'], '1353003037': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_4.jpg'], '1353003038': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_5.jpg'], '1353003039': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_6.jpg'], '1353003040': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_7.jpg'], '1353003041': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_8.jpg'], '1353003042': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_9.jpg'], '1353003043': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_10.jpg'], '1353003044': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_11.jpg'], '1353003045': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_12.jpg'], '1353003046': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_13.jpg'], '1353003047': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_14.jpg'], '1353003048': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_15.jpg'], '1353003049': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_16.jpg'], '1353003050': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_17.jpg'], '1353003051': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_18.jpg'], '1353003052': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_19.jpg'], '1353003053': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_20.jpg'], '1353003054': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_21.jpg'], '1353003055': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_22.jpg'], '1353003056': ['temp/1745400440_1620143_1008283903_6d008d31a1477b2e98cbafa96bd48e53_23.jpg']} batch 1 Loaded 7 chid ids of type : 3243 ++++++++++++++fin du test de tile ############################### TEST rotate_chi ################################ test rotate avec chi rectangles, rles, polygones Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : rotate list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.3028297424316406 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:rotate Wed Apr 23 11:27:45 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Beginning of datou_step_rotate ! Warning, new_feed_id is empty ! We are in a linear step without datou_depend ! rotate photos of 0,90,180,270 degres batch 1 Loaded 16 chid ids of type : 3086 ++++++++++++++++ map_chi of length : 1 https://marlene.fotonower.com/api/v1/secured/portfolio/new?access_token=78d09a0790ec6ecbf119343125a81fdc feed_id_new_photos : 22341680 time for calcul the mask position with numpy : 0.010390281677246094 nb_pixel_total : 110633 time to create 1 rle with old method : 0.2697007656097412 .time for calcul the mask position with numpy : 0.009696006774902344 nb_pixel_total : 15826 time to create 1 rle with old method : 0.036574602127075195 .time for calcul the mask position with numpy : 0.009908676147460938 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012546300888061523 .time for calcul the mask position with numpy : 0.009271383285522461 nb_pixel_total : 1633 time to create 1 rle with old method : 0.003812074661254883 .time for calcul the mask position with numpy : 0.010019063949584961 nb_pixel_total : 105533 time to create 1 rle with old method : 0.24838471412658691 .time for calcul the mask position with numpy : 0.010016918182373047 nb_pixel_total : 4393 time to create 1 rle with old method : 0.012082815170288086 .time for calcul the mask position with numpy : 0.009479045867919922 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015184879302978516 .time for calcul the mask position with numpy : 0.009798526763916016 nb_pixel_total : 62627 time to create 1 rle with old method : 0.16097688674926758 .time for calcul the mask position with numpy : 0.009333610534667969 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07648253440856934 .time for calcul the mask position with numpy : 0.00969243049621582 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08566498756408691 .time for calcul the mask position with numpy : 0.010749340057373047 nb_pixel_total : 48775 time to create 1 rle with old method : 0.12825870513916016 .time for calcul the mask position with numpy : 0.0703272819519043 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.14681267738342285 .time for calcul the mask position with numpy : 0.009261131286621094 nb_pixel_total : 2310 time to create 1 rle with old method : 0.00531768798828125 .time for calcul the mask position with numpy : 0.009393453598022461 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005412101745605469 .time for calcul the mask position with numpy : 0.01013493537902832 nb_pixel_total : 3112 time to create 1 rle with old method : 0.008234024047851562 .time for calcul the mask position with numpy : 0.009459257125854492 nb_pixel_total : 1662 time to create 1 rle with old method : 0.0039899349212646484 .Needs to change image size ! time for calcul the mask position with numpy : 0.00982356071472168 nb_pixel_total : 110633 time to create 1 rle with old method : 0.25171446800231934 .time for calcul the mask position with numpy : 0.010645627975463867 nb_pixel_total : 15826 time to create 1 rle with old method : 0.036102294921875 .time for calcul the mask position with numpy : 0.010434865951538086 nb_pixel_total : 5286 time to create 1 rle with old method : 0.015379667282104492 .time for calcul the mask position with numpy : 0.009257078170776367 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0038046836853027344 .time for calcul the mask position with numpy : 0.010218620300292969 nb_pixel_total : 105533 time to create 1 rle with old method : 0.26938796043395996 .time for calcul the mask position with numpy : 0.009267330169677734 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010194778442382812 .time for calcul the mask position with numpy : 0.009432315826416016 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015475749969482422 .time for calcul the mask position with numpy : 0.00998830795288086 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14363598823547363 .time for calcul the mask position with numpy : 0.010231256484985352 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07639789581298828 .time for calcul the mask position with numpy : 0.009439229965209961 nb_pixel_total : 37724 time to create 1 rle with old method : 0.0888819694519043 .time for calcul the mask position with numpy : 0.009770631790161133 nb_pixel_total : 48775 time to create 1 rle with old method : 0.1100151538848877 .time for calcul the mask position with numpy : 0.039652109146118164 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.18616795539855957 .time for calcul the mask position with numpy : 0.009467601776123047 nb_pixel_total : 2310 time to create 1 rle with old method : 0.006635427474975586 .time for calcul the mask position with numpy : 0.011061906814575195 nb_pixel_total : 2256 time to create 1 rle with old method : 0.005385398864746094 .time for calcul the mask position with numpy : 0.010795116424560547 nb_pixel_total : 3112 time to create 1 rle with old method : 0.008048534393310547 .time for calcul the mask position with numpy : 0.009222745895385742 nb_pixel_total : 1662 time to create 1 rle with old method : 0.003911256790161133 .time for calcul the mask position with numpy : 0.009781599044799805 nb_pixel_total : 110633 time to create 1 rle with old method : 0.25544261932373047 .time for calcul the mask position with numpy : 0.00995492935180664 nb_pixel_total : 15826 time to create 1 rle with old method : 0.037351369857788086 .time for calcul the mask position with numpy : 0.01074838638305664 nb_pixel_total : 5286 time to create 1 rle with old method : 0.012714385986328125 .time for calcul the mask position with numpy : 0.009493827819824219 nb_pixel_total : 1633 time to create 1 rle with old method : 0.005735635757446289 .time for calcul the mask position with numpy : 0.010477304458618164 nb_pixel_total : 105533 time to create 1 rle with old method : 0.26859092712402344 .time for calcul the mask position with numpy : 0.009180545806884766 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010133743286132812 .time for calcul the mask position with numpy : 0.01089334487915039 nb_pixel_total : 632 time to create 1 rle with old method : 0.0018603801727294922 .time for calcul the mask position with numpy : 0.009435176849365234 nb_pixel_total : 62627 time to create 1 rle with old method : 0.14037203788757324 .time for calcul the mask position with numpy : 0.009344816207885742 nb_pixel_total : 33681 time to create 1 rle with old method : 0.0805671215057373 .time for calcul the mask position with numpy : 0.010571479797363281 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08962821960449219 .time for calcul the mask position with numpy : 0.010323762893676758 nb_pixel_total : 48775 time to create 1 rle with old method : 0.13039803504943848 .time for calcul the mask position with numpy : 0.05844569206237793 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.16824102401733398 .time for calcul the mask position with numpy : 0.014536619186401367 nb_pixel_total : 2310 time to create 1 rle with old method : 0.008708000183105469 .time for calcul the mask position with numpy : 0.00967860221862793 nb_pixel_total : 2256 time to create 1 rle with old method : 0.006492137908935547 .time for calcul the mask position with numpy : 0.010003805160522461 nb_pixel_total : 3112 time to create 1 rle with old method : 0.007307529449462891 .time for calcul the mask position with numpy : 0.009139537811279297 nb_pixel_total : 1662 time to create 1 rle with old method : 0.004476785659790039 .Needs to change image size ! time for calcul the mask position with numpy : 0.011430501937866211 nb_pixel_total : 110633 time to create 1 rle with old method : 0.31302356719970703 .time for calcul the mask position with numpy : 0.013892889022827148 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03554201126098633 .time for calcul the mask position with numpy : 0.008829593658447266 nb_pixel_total : 5286 time to create 1 rle with old method : 0.013127326965332031 .time for calcul the mask position with numpy : 0.008941888809204102 nb_pixel_total : 1633 time to create 1 rle with old method : 0.0036733150482177734 .time for calcul the mask position with numpy : 0.009887456893920898 nb_pixel_total : 105533 time to create 1 rle with old method : 0.24808311462402344 .time for calcul the mask position with numpy : 0.009593725204467773 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010919570922851562 .time for calcul the mask position with numpy : 0.00931859016418457 nb_pixel_total : 632 time to create 1 rle with old method : 0.0015430450439453125 .time for calcul the mask position with numpy : 0.010806560516357422 nb_pixel_total : 62627 time to create 1 rle with old method : 0.15250468254089355 .time for calcul the mask position with numpy : 0.009569883346557617 nb_pixel_total : 33681 time to create 1 rle with old method : 0.0833425521850586 .time for calcul the mask position with numpy : 0.011295080184936523 nb_pixel_total : 37724 time to create 1 rle with old method : 0.09717750549316406 .time for calcul the mask position with numpy : 0.010531187057495117 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11292839050292969 .time for calcul the mask position with numpy : 0.053356170654296875 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.4650847911834717 .time for calcul the mask position with numpy : 0.009775638580322266 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0066492557525634766 .time for calcul the mask position with numpy : 0.01189279556274414 nb_pixel_total : 2256 time to create 1 rle with old method : 0.007287025451660156 .time for calcul the mask position with numpy : 0.010551691055297852 nb_pixel_total : 3112 time to create 1 rle with old method : 0.009760379791259766 .time for calcul the mask position with numpy : 0.009386301040649414 nb_pixel_total : 1662 time to create 1 rle with old method : 0.003882169723510742 . About to upload 4 photos upload in portfolio : 22341680 init cache_photo without model_param we have 4 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400487_1620143 we have uploaded 4 photos in the portfolio 22341680 time of upload the photos Elapsed time : 1.460582971572876 Len new_chis : 4 Len list_new_chi_with_photo_id : 64 of type : 3230 batch 1 Loaded 64 chid ids of type : 3230 Number RLEs to save : 24654 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 24.835557222366333 time spend to save output : 0.00017690658569335938 total time spend for step 1 : 24.835734128952026 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1353003132: ['1003369118', 'temp/1745400465_1620143_1003369118_58171420504d0b5f05a1233b6c515509_658263370.jpg', [, , , , , , , , , , , , , , , ]], 1353003133: ['1003369118', 'temp/1745400465_1620143_1003369118_58171420504d0b5f05a1233b6c515509_6582633790.jpg', [, , , , , , , , , , , , , , , ]], 1353003135: ['1003369118', 'temp/1745400465_1620143_1003369118_58171420504d0b5f05a1233b6c515509_65826337180.jpg', [, , , , , , , , , , , , , , , ]], 1353003136: ['1003369118', 'temp/1745400465_1620143_1003369118_58171420504d0b5f05a1233b6c515509_65826337270.jpg', [, , , , , , , , , , , , , , , ]]} batch 1 Loaded 64 chid ids of type : 3230 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++fin du test de rotate_chi Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : rubbia_carac_pet_clair_0121 not run because too long ############################### TEST rubbia_carac_pet_clair_0121_no_cnn ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 6479 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 6480 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7445 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 6509 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 6480 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 6479 doesn't seem to be define in the database( WARNING : type of input 1 of step 7445 doesn't seem to be define in the database( WARNING : type of output 1 of step 7445 doesn't seem to be define in the database( WARNING : type of input 3 of step 6509 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! DataTypes for each output/input checked ! List Step Type Loaded in datou : merge_mask_thcl_custom, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.39734768867492676 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:merge_mask_thcl_custom Wed Apr 23 11:28:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step merge_mask_thcl_custom batch 1 Loaded 82 chid ids of type : 2800 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++As expected we have just one thcl present begin to find the sub_photo_id : begin to find the sub_photo_id : batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! batch 1 Loaded 76 chid ids of type : 2913 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++End of step merge_mask_thcl_custom time spend for datou_step_exec : 0.36348414421081543 time spend to save output : 0.00013637542724609375 total time spend for step 1 : 0.3636205196380615 step2:rle_unique_nms_with_priority Wed Apr 23 11:28:11 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 43 chid ids of type : 2913 +++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 3.072178840637207 create new chi : 5.7697296142578125e-05 time to delete rle : 0.035515785217285156 save time : 3.1948089599609375e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 3.003903388977051 create new chi : 5.841255187988281e-05 time to delete rle : 0.018660306930541992 save time : 4.3392181396484375e-05 map_output_result : {1009068683: (0.002588053987919006, 'Should be the crop_list due to order', 0.005176107975838012), 1009068724: (0.002588053987919006, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 6.336039066314697 time spend to save output : 9.751319885253906e-05 total time spend for step 2 : 6.33613657951355 step3:ventilate_hashtags_in_portfolio Wed Apr 23 11:28:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 3373196 get user id for portfolio 3373196 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3373196 AND mptpi.`type`=2913 AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.5067811012268066 time spend to save output : 7.939338684082031e-05 total time spend for step 3 : 0.5068604946136475 step4:final Wed Apr 23 11:28:18 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! Catched exception ! Connect or reconnect ! time spend for datou_step_exec : 0.10035538673400879 time spend to save output : 5.698204040527344e-05 total time spend for step 4 : 0.10041236877441406 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} new output for save of step final : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} [1009068683, 1009068724] Looping around the photos to save general results len do output : 2 /1009068683.Didn't retrieve data . /1009068724.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068683', None, None, None, None, None, None) ('2719', None, None, None, None, None, None, None, None) ('2719', '3373196', '1009068724', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.012633085250854492 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} {1009068683: ('0.0025316977085267163',), 1009068724: ('0.0025316977085267163',)} ############################### TEST rubbia_carac_jrm_no_mask_detect ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! Step 7557 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 7556 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 7561 merge_mask_and_thcl is not consistent : 3 used against 1 in the step definition ! WARNING : number of inputs for step 7558 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7560 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7559 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7560 doesn't seem to be define in the database( WARNING : type of input 3 of step 7559 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7556 have datatype=6 whereas input 0 of step 7561 have datatype=20 WARNING : type of output 1 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7558 doesn't seem to be define in the database( WARNING : type of output 2 of step 7561 doesn't seem to be define in the database( WARNING : type of input 1 of step 7560 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : crop_condition, thcl, argmax, merge_mask_and_thcl, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBBBBFBFBFBFBFBFBFFBFFBFFBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 14 ; length of list_pids : 14 ; length of list_args : 14 time to download the photos : 1.4959073066711426 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 7 step1:crop_condition Wed Apr 23 11:28:19 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 3336 Loading chi in step crop for list_pids : 14 ! batch 1 Loaded 121 chid ids of type : 3336 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ begin to crop the class : teint_dans_la_masse param for this class : {'min_score': 0.7} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : teint_dans_la_masse begin to crop the class : autre_refus param for this class : {'min_score': 0.7} filtre for class : autre_refus hashtag_id of this class : 2107752406 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : autre_refus begin to crop the class : carton_gris param for this class : {'min_score': 0.7} filtre for class : carton_gris hashtag_id of this class : 2107753020 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_gris begin to crop the class : cartonnette param for this class : {'min_score': 0.7} filtre for class : cartonnette hashtag_id of this class : 702398920 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : cartonnette begin to crop the class : carton_brun param for this class : {'min_score': 0.7} filtre for class : carton_brun hashtag_id of this class : 2107753024 map_result returned by crop_photo_return_map_crop : length : 0 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : carton_brun begin to crop the class : plastique param for this class : {'min_score': 0.7} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : kraft param for this class : {'min_score': 0.7} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : metal param for this class : {'min_score': 0.7} filtre for class : metal hashtag_id of this class : 492628673 time spend for datou_step_exec : 10.869347095489502 time spend to save output : 0.0009682178497314453 total time spend for step 1 : 10.870315313339233 step2:thcl Wed Apr 23 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou step Thcl ! no input time spend for datou_step_exec : 0.0008029937744140625 time spend to save output : 4.792213439941406e-05 total time spend for step 2 : 0.0008509159088134766 step3:argmax Wed Apr 23 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! Beginning of datou_step Argmax ! no input time spend for datou_step_exec : 6.604194641113281e-05 time spend to save output : 1.1682510375976562e-05 total time spend for step 3 : 7.772445678710938e-05 step4:merge_mask_and_thcl Wed Apr 23 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 VR 22-3-18 : For now we do not clean correctly the datou structure No keys ! debut de la step merge mask and classif time spend for datou_step_exec : 0.00014400482177734375 time spend to save output : 1.0967254638671875e-05 total time spend for step 4 : 0.00015497207641601562 step5:rle_unique_nms_with_priority Wed Apr 23 11:28:30 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 26 chid ids of type : 3418 ++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 3.323396682739258 create new chi : 0.008732795715332031 time to delete rle : 0.8926601409912109 save time : 2.574920654296875e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 3.051145315170288 create new chi : 0.008669137954711914 time to delete rle : 0.33792781829833984 save time : 5.2928924560546875e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.1958069801330566 create new chi : 0.00665736198425293 time to delete rle : 2.3310508728027344 save time : 3.981590270996094e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.5571975708007812 create new chi : 4.839897155761719e-05 time to delete rle : 0.5202879905700684 save time : 1.9311904907226562e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.6499748229980469 create new chi : 2.6464462280273438e-05 time to delete rle : 0.6395857334136963 save time : 2.002716064453125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.7689361572265625 create new chi : 5.888938903808594e-05 time to delete rle : 0.6873140335083008 save time : 9.059906005859375e-06 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.4841232299804688 create new chi : 0.007283449172973633 time to delete rle : 0.759770393371582 save time : 4.744529724121094e-05 nb_obj : 0 nb_hashtags : 2 time to prepare the origin masks : 2.064009428024292 create new chi : 0.006898641586303711 time to delete rle : 1.36698317527771 save time : 1.4543533325195312e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.6424028873443604 create new chi : 2.4557113647460938e-05 time to delete rle : 0.7506024837493896 save time : 1.4543533325195312e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.7060365676879883 create new chi : 0.007002353668212891 time to delete rle : 0.9986753463745117 save time : 5.221366882324219e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.5631327629089355 create new chi : 3.981590270996094e-05 time to delete rle : 0.7580420970916748 save time : 8.58306884765625e-06 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.9216606616973877 create new chi : 0.00781869888305664 time to delete rle : 0.5367131233215332 save time : 3.814697265625e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 2.2242257595062256 create new chi : 4.315376281738281e-05 time to delete rle : 1.4444043636322021 save time : 1.239776611328125e-05 nb_obj : 0 nb_hashtags : 1 time to prepare the origin masks : 1.8223633766174316 create new chi : 3.2901763916015625e-05 time to delete rle : 0.5473990440368652 save time : 1.8358230590820312e-05 map_output_result : {1008921601: (0.005230650193135238, 'Should be the crop_list due to order', 0.01189037070001202), 1008921600: (0.005230650193135238, 'Should be the crop_list due to order', 0.012566967797237925), 1008921657: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921656: (0.005230650193135238, 'Should be the crop_list due to order', 0.0018834633354450428), 1008921602: (0.005230650193135238, 'Should be the crop_list due to order', 0.011059624445676274), 1008922130: (0.005230650193135238, 'Should be the crop_list due to order', 0.009172696586949636), 1008922101: (0.005230650193135238, 'Should be the crop_list due to order', 0.001800476457962238), 1008922097: (0.005230650193135238, 'Should be the crop_list due to order', 0.0021337986371828908), 1008922003: (0.005230650193135238, 'Should be the crop_list due to order', 0.010260185698447893), 1008922002: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008921786: (0.005230650193135238, 'Should be the crop_list due to order', 0.0024127929996832437), 1008922095: (0.005230650193135238, 'Should be the crop_list due to order', 0.0), 1008922073: (0.005230650193135238, 'Should be the crop_list due to order', 0.0034527686490338928), 1008922072: (0.005230650193135238, 'Should be the crop_list due to order', 0.006595957396262274)} End step rle-unique-nms time spend for datou_step_exec : 43.741974115371704 time spend to save output : 0.03242373466491699 total time spend for step 5 : 43.77439785003662 step6:ventilate_hashtags_in_portfolio Wed Apr 23 11:29:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! To do loadFromThcl(), then load ParamDescType : thcl2456 thcls : [{'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'}] thcl {'id': 2456, 'mtr_user_id': 31, 'name': 'learn_qualipapia_papier_refus_from_vlg_data_aug', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'papier,refus', 'svm_portfolios_learning': '3028087,3028251', 'photo_hashtag_type': 3049, 'photo_desc_type': 4999, 'type_classification': 'caffe', 'hashtag_id_list': '492668766,538914404'} Update svm_hashtag_type_desc : 4999 Iterating over portfolio : 3364276 get user id for portfolio 3364276 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3535038 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=3364276 AND mptpi.`type`=3418 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','refus')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.17602229118347168 time spend to save output : 9.822845458984375e-05 total time spend for step 6 : 0.17612051963806152 step7:final Wed Apr 23 11:29:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.019312381744384766 time spend to save output : 7.915496826171875e-05 total time spend for step 7 : 0.019391536712646484 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',)} new output for save of step final : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',)} [1008921601, 1008921600, 1008921657, 1008921656, 1008921602, 1008922130, 1008922101, 1008922097, 1008922003, 1008922002, 1008921786, 1008922095, 1008922073, 1008922072] Looping around the photos to save general results len do output : 14 /1008921601.Didn't retrieve data . /1008921600.Didn't retrieve data . /1008921657.Didn't retrieve data . /1008921656.Didn't retrieve data . /1008921602.Didn't retrieve data . /1008922130.Didn't retrieve data . /1008922101.Didn't retrieve data . /1008922097.Didn't retrieve data . /1008922003.Didn't retrieve data . /1008922002.Didn't retrieve data . /1008921786.Didn't retrieve data . /1008922095.Didn't retrieve data . /1008922073.Didn't retrieve data . /1008922072.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921601', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921600', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921657', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921656', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921602', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922130', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922101', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922097', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922003', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922002', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008921786', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922095', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922073', None, None, None, None, None, None) ('3164', None, None, None, None, None, None, None, None) ('3164', '3364276', '1008922072', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 42 time used for this insertion : 0.01897406578063965 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 7 output : {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',)} {1008921601: ('0.005252307643909098',), 1008921600: ('0.005252307643909098',), 1008921657: ('0.005252307643909098',), 1008921656: ('0.005252307643909098',), 1008921602: ('0.005252307643909098',), 1008922130: ('0.005252307643909098',), 1008922101: ('0.005252307643909098',), 1008922097: ('0.005252307643909098',), 1008922003: ('0.005252307643909098',), 1008922002: ('0.005252307643909098',), 1008921786: ('0.005252307643909098',), 1008922095: ('0.005252307643909098',), 1008922073: ('0.005252307643909098',), 1008922072: ('0.005252307643909098',)} ############################### TEST ventilate_hashtags_in_portfolio ################################ DELETE FROM MTRUser.mtr_portfolio_photos where mtr_portfolio_id = 5486631; Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.014998435974121094 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:ventilate_hashtags_in_portfolio Wed Apr 23 11:29:14 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 5363525 get user id for portfolio 5363525 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=5363525 AND mptpi.`type`=4268 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','error','environment')) AND mptpi.`min_score`=0.3 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.07050204277038574 time spend to save output : 0.00016832351684570312 total time spend for step 1 : 0.07067036628723145 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1075306598, 1075306564, 1075306534, 1075306522, 1075304668] Looping around the photos to save general results len do output : 1 /5363525. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306598', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306564', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306534', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075306522', None, None, None, None, None, None) ('3070', None, None, None, None, None, None, None, None) ('3070', '5363525', '1075304668', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.015918493270874023 save_final save missing photos in datou_result : After save, about to update current ! Ayatollah of tests excluded it ! (Bon le prochain developpeur qui passe ici peut enlever ayatollah VR 11-2-21) name : merge_qualipapia_like not run because too long ############################### TEST poly_ro_rle ################################ test creation de rle a partir de polygon Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : poly_to_rle list_input_json : [] origin We have 1 , BFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 1 ; length of list_pids : 1 ; length of list_args : 1 time to download the photos : 0.2391948699951172 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:poly_to_rle Wed Apr 23 11:29:15 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec can't find the hashtag_type_input ,set the output_type same as the input_type batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++time for calcul the mask position with numpy : 0.00898122787475586 nb_pixel_total : 110633 time to create 1 rle with old method : 0.25523805618286133 time for calcul the mask position with numpy : 0.0074651241302490234 nb_pixel_total : 15826 time to create 1 rle with old method : 0.03547167778015137 time for calcul the mask position with numpy : 0.00700688362121582 nb_pixel_total : 5286 time to create 1 rle with old method : 0.01303863525390625 time for calcul the mask position with numpy : 0.007136821746826172 nb_pixel_total : 1633 time to create 1 rle with old method : 0.004026174545288086 time for calcul the mask position with numpy : 0.007897615432739258 nb_pixel_total : 105533 time to create 1 rle with old method : 0.25582218170166016 time for calcul the mask position with numpy : 0.007409334182739258 nb_pixel_total : 4393 time to create 1 rle with old method : 0.010618925094604492 time for calcul the mask position with numpy : 0.007676601409912109 nb_pixel_total : 632 time to create 1 rle with old method : 0.0016467571258544922 time for calcul the mask position with numpy : 0.008060932159423828 nb_pixel_total : 62627 time to create 1 rle with old method : 0.15035676956176758 time for calcul the mask position with numpy : 0.008116722106933594 nb_pixel_total : 33681 time to create 1 rle with old method : 0.07910394668579102 time for calcul the mask position with numpy : 0.008014440536499023 nb_pixel_total : 37724 time to create 1 rle with old method : 0.08878731727600098 time for calcul the mask position with numpy : 0.007941007614135742 nb_pixel_total : 48775 time to create 1 rle with old method : 0.11541461944580078 time for calcul the mask position with numpy : 0.04527401924133301 nb_pixel_total : 1171703 time to create 1 rle with new method : 0.25053930282592773 time for calcul the mask position with numpy : 0.0070781707763671875 nb_pixel_total : 2310 time to create 1 rle with old method : 0.0050506591796875 time for calcul the mask position with numpy : 0.006873369216918945 nb_pixel_total : 2256 time to create 1 rle with old method : 0.0054340362548828125 time for calcul the mask position with numpy : 0.007246494293212891 nb_pixel_total : 3112 time to create 1 rle with old method : 0.0071909427642822266 time for calcul the mask position with numpy : 0.007225513458251953 nb_pixel_total : 1662 time to create 1 rle with old method : 0.003994464874267578 batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 1.7134263515472412 time spend to save output : 9.894371032714844e-05 total time spend for step 1 : 1.7135252952575684 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1003369118: 'temp/1745400554_1620143_1003369118_58171420504d0b5f05a1233b6c515509_65826337.jpg'} batch 1 Loaded 16 chid ids of type : 3391 ++++++++++++++++fin du test de poly_to_rle ############################### TEST cod_sts ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : split_time_score list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.01464986801147461 About to test input to load Calling datou_exec Inside datou_exec : verbose : False we use local cache db, so we are in local job, but when commit will be implemented for local cache db, we could again use save number of steps : 1 step1:split_time_score Wed Apr 23 11:29:17 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec TODO : Insert select and so on Begin split_port_in_batch_balle thcls : [{'id': 861, 'mtr_user_id': 31, 'name': 'Rungis_class_dechets_1212', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': 'Rungis_Aluminium,Rungis_Carton,Rungis_Papier,Rungis_Plastique_clair,Rungis_Plastique_dur,Rungis_Plastique_fonce,Rungis_Tapis_vide,Rungis_Tetrapak', 'svm_portfolios_learning': '1160730,571842,571844,571839,571933,571840,571841,572307', 'photo_hashtag_type': 999, 'photo_desc_type': 3963, 'type_classification': 'caffe', 'hashtag_id_list': '2107751280,2107750907,2107750908,2107750909,2107750910,2107750911,2107750912,2107750913'}] thcls : [{'id': 758, 'mtr_user_id': 31, 'name': 'Rungis_amount_dechets_fall_2018_v2', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '05102018_Papier_non_papier_dense,05102018_Papier_non_papier_peu_dense,05102018_Papier_non_papier_presque_vide,05102018_Papier_non_papier_tres_dense,05102018_Papier_non_papier_tres_peu_dense', 'svm_portfolios_learning': '1108385,1108386,1108388,1108384,1108387', 'photo_hashtag_type': 856, 'photo_desc_type': 3853, 'type_classification': 'caffe', 'hashtag_id_list': '2107751013,2107751014,2107751015,2107751016,2107751017'}] (('48', 4), ('42', 3)) ERROR counted https://github.com/fotonower/Velours/issues/663#issuecomment-421136223 {} 17082021 4453840 Nombre de photos uploadées : 7 / 23040 (0%) 17082021 4453840 Nombre de photos taguées (types de déchets): 0 / 7 (0%) 17082021 4453840 Nombre de photos taguées (volume) : 0 / 7 (0%) elapsed_time : load_data_split_time_score 5.0067901611328125e-06 elapsed_time : order_list_meta_photo_and_scores 1.0251998901367188e-05 ??????? elapsed_time : fill_and_build_computed_from_old_data 0.0008935928344726562 elapsed_time : insert_dashboard_record_day_entry 0.02440953254699707 TODO 20-09-21 https://github.com/fotonower/raspi-fotonower-x/issues/253#issuecomment-923099773 TODO 20-9-21 TODO 20-9-21 ***** BEGIN SPLIT TIME ***** ```````list printed: [[0, 1, 2, 3], [4, 5, 6]] forced_hashtag: jrm force hashtag to jrm elapsed_time : SPLIT_TIME 0.00592494010925293 ***** END SPLIT TIME ***** NUMBER BATCH : 2 list_ponderation used : [0.001, 0.001, 0.001, 0.001, 0.001] , list_hashtag_class_create_as_list : ['jrm'] ERROR missing amount info ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 24.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_094808.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 ERROR missing amount info ERROR missing amount info ERROR missing amount info result_one_balle_Type_jrm:{'day': '17082021', 'map_nb_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'map_time_amount': {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}, 'duration': 3.0, 'nb_balles_papier': 0, 'begin_time_port': 'IMG_20210817_104213.jpg'} Production hashtag (incorrect ponderation at 20-10-18) : 0 We have rejected 0 photos because of the batch_size condition ! NUMBER BATCH list_of_portfolios_to_create : 2 list_same_port_ids : [4453926] find same portfolio which already exist 4453926 , we will use it list_same_port_ids : [4652336] find same portfolio which already exist 4652336 , we will use it # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4453926 AND mptpi.`type`=4038 To do # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 8564 mask_detect is not consistent : 4 used against 2 in the step definition ! WARNING : number of outputs for step 8572 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8573 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 8567 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 8567 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 8566 argmax have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 8568 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8569 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 9453 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 9453 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 8571 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 8570 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 8570 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 8574 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Step 9126 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 8564 doesn't seem to be define in the database( WARNING : type of input 2 of step 8567 doesn't seem to be define in the database( WARNING : output 0 of step 8566 have datatype=6 whereas input 2 of step 8568 have datatype=5 WARNING : output 1 of step 8564 have datatype=2 whereas input 1 of step 8568 have datatype=7 WARNING : output 0 of step 8564 have datatype=16 whereas input 0 of step 8568 have datatype=1 WARNING : type of output 2 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 8568 doesn't seem to be define in the database( WARNING : type of input 1 of step 8571 doesn't seem to be define in the database( WARNING : type of output 1 of step 8571 doesn't seem to be define in the database( WARNING : type of input 3 of step 8570 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 8571 have datatype=10 whereas input 2 of step 8574 have datatype=6 WARNING : type of input 2 of step 9453 doesn't seem to be define in the database( WARNING : output 1 of step 8569 have datatype=7 whereas input 2 of step 9453 have datatype=None WARNING : type of output 3 of step 9453 doesn't seem to be define in the database( WARNING : type of input 2 of step 8571 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8572 doesn't seem to be define in the database( WARNING : type of output 3 of step 8564 doesn't seem to be define in the database( WARNING : type of input 1 of step 8573 doesn't seem to be define in the database( WARNING : type of output 1 of step 8572 doesn't seem to be define in the database( WARNING : type of input 3 of step 8567 doesn't seem to be define in the database( WARNING : type of output 1 of step 8573 doesn't seem to be define in the database( WARNING : type of input 4 of step 8567 doesn't seem to be define in the database( DataTypes for each output/input checked ! TODO Duplicate data, are they consistent 3 ? Duplicate data, are they consistent 4 ? SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4652336 AND mptpi.`type`=4038 To do elapsed_time : count_nb_balles_and_create_portfolio 1.081723690032959 # DISPLAY ALL COLLECTED DATA : {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}} time spend for datou_step_exec : 1.1622166633605957 time spend to save output : 9.036064147949219e-05 total time spend for step 1 : 1.1623070240020752 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : split_time_score we use saveGeneral [1050302186, 1050302153, 1050302152, 1050302146, 1050302113, 1050302110, 1050302106] Looping around the photos to save general results len do output : 1 /4453840Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302186', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302153', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302152', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302146', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302113', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302110', None, None, None, None, None, None) ('3781', None, None, None, None, None, None, None, None) ('3781', '4453840', '1050302106', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 8 time used for this insertion : 0.013545036315917969 save_final save missing photos in datou_result : After save, about to update current ! Result test cod : {4453840: ([[0, 1, 2, 3], [4, 5, 6]], {'Rungis_jrm': [(0, 1), (1, 2)]}, {4453926: {'list_of_photos': [1050302106, 1050302146, 1050302110, 1050302152], 'hashtag': 'jrm'}, 4652336: {'list_of_photos': [1050302113, 1050302153, 1050302186], 'hashtag': 'jrm'}}, {2107757407: 7}, {'amount_uploaded_and_tagged': {'17082021': {'nb_upload': 7, 'nb_taggue_class': 0, 'nb_taggue_densite': 0}}, 'map_amount_per_hashtag': {'Rungis_jrm': [(0, 1), (1, 2)]}, 'count': {'Rungis_jrm': [(0, 1), (1, 2)]}})}| ############################### TEST cod_download ################################ warning , we can't find thcl infos in json_data warning , we can't find pdt infos in json_data [] [] ############################### TEST sendgrid ################################ test sendgrid senders@fotonower.com no problem of authentification, for test if the email can be received, try with a real receiver fin du test de sendgrid ############################### TEST rym_consolidate ################################ test_rym_consolidate Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 9321 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9357 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 9318 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 9318 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 9410 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 9319 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 9328 blur_detection have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9327 brightness have less inputs used (0) than in the step definition (1) : maybe we manage optionnal inputs ! Step 9326 send_mail_cod have less inputs used (4) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9321 have datatype=11 whereas input 0 of step 9318 have datatype=2 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 1 of step 9318 doesn't seem to be define in the database( WARNING : type of output 1 of step 9357 doesn't seem to be define in the database( WARNING : type of input 3 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 9410 doesn't seem to be define in the database( WARNING : output 1 of step 9318 have datatype=7 whereas input 1 of step 9410 have datatype=None WARNING : type of output 1 of step 9410 doesn't seem to be define in the database( WARNING : type of input 4 of step 9319 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 9410 have datatype=10 whereas input 3 of step 9326 have datatype=6 WARNING : type of output 1 of step 9321 doesn't seem to be define in the database( WARNING : type of input 1 of step 9357 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final, blur_detection, brightness, send_mail_cod list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.2656271457672119 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 8 step1:copy_chis Wed Apr 23 11:29:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 0 chid ids of type : 0 time spend for datou_step_exec : 0.0047724246978759766 time spend to save output : 4.839897155761719e-05 total time spend for step 1 : 0.004820823669433594 step2:consolidate_hashtags_from_manual_portfolio Wed Apr 23 11:29:21 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 4709558 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4683188 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673496 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673497 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673498 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673500 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673501 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673502 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673503 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673504 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673505 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673506 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673507 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4673508 AND mpp.hide_status=0 ORDER BY ph.size desc To test ! Use context local managing function ! time spend for datou_step_exec : 5.012671947479248 time spend to save output : 0.00010561943054199219 total time spend for step 2 : 5.01277756690979 step3:rle_unique_nms_with_priority Wed Apr 23 11:29:26 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 65 chid ids of type : 4016 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 21.38648271560669 create new chi : 5.7697296142578125e-05 time to delete rle : 0.4010181427001953 save time : 5.459785461425781e-05 nb_obj : 0 nb_hashtags : 3 time to prepare the origin masks : 20.088388442993164 create new chi : 5.316734313964844e-05 time to delete rle : 0.2831902503967285 save time : 4.76837158203125e-05 map_output_result : {1057289546: (0.0, 'Should be the crop_list due to order', 0.0), 1057289467: (0.0, 'Should be the crop_list due to order', 0.0)} End step rle-unique-nms time spend for datou_step_exec : 42.49069356918335 time spend to save output : 0.00033926963806152344 total time spend for step 3 : 42.49103283882141 step4:ventilate_hashtags_in_portfolio Wed Apr 23 11:30:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 4709558 get user id for portfolio 4709558 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=4709558 AND mptpi.`type`=4016 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('environnement','mal_croppe','pet_clair','etiquette','bouchon','pehd','barquette_avec_film','metal','pet_fonce','aluminium','carton','film_plastique','papier','autre')) AND mptpi.`min_score`=0.7 To do To do ! Use context local managing function ! time spend for datou_step_exec : 0.2094438076019287 time spend to save output : 0.00013947486877441406 total time spend for step 4 : 0.20958328247070312 step5:final Wed Apr 23 11:30:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.025732040405273438 time spend to save output : 5.602836608886719e-05 total time spend for step 5 : 0.025788068771362305 step6:blur_detection Wed Apr 23 11:30:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step blur_detection toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.0051631927490234375 time spend to save output : 5.078315734863281e-05 total time spend for step 6 : 0.00521397590637207 step7:brightness Wed Apr 23 11:30:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure inside step calcul brightness toutes les photos sont déjà traitées, on saute les calculs time spend for datou_step_exec : 0.0048406124114990234 time spend to save output : 7.033348083496094e-05 total time spend for step 7 : 0.004910945892333984 step8:send_mail_cod Wed Apr 23 11:30:08 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 complete output_args for input 1 complete output_args for input 2 Inconsistent number of input and output, step which parrallelize and manage error in input by avoiding sending an output for this data can't be used in tree dependencies of input and output complete output_args for input 3 We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure dans la step send mail cod work_area: /home/admin/temp in order to get the selector url, please entre the license of selector results_COD_P4709558_23-04-2025_11_30_08.pdf 4673494 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734941745400608 4673496 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734961745400609 4673497 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734971745400611 4673498 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46734981745400613 4673500 change filename to text .change filename to text .imagette46735001745400616 4673501 change filename to text .imagette46735011745400617 4673502 imagette46735021745400617 4673503 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735031745400617 4673504 imagette46735041745400619 4673505 imagette46735051745400619 4673506 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735061745400619 4673507 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735071745400621 4673508 change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .change filename to text .imagette46735081745400622 velour_link : ce batch n'est pas dans un dashboard, on n'envoi pas de mail. si vous voulez quand même des mail , veuillez configurer no_mail = 2 args[1057289546] : ((1057289546, -4.333383571220791, 492609224), (1057289546, -0.5998675991292823, 501862349), '0.009511382621534484') We are sending mail with results at marine@fotonower.com args[1057289467] : ((1057289467, -4.424440243329978, 492609224), (1057289467, -0.4062218880770088, 496442774), '0.009511382621534484') We are sending mail with results at marine@fotonower.com refus_total : 0.009511382621534484 SELECT ph.photo_id,ph.url,ph.username,ph.uploaded_at,ph.text FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4709558 AND mpp.hide_status=0 ORDER BY mpp.order LIMIT 0, 1000 current_id not found list index out of range start upload file to ovh https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_23-04-2025_11_30_08.pdf results_COD_P4709558_23-04-2025_11_30_08.pdf uploaded to url https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_23-04-2025_11_30_08.pdf start insert file to database insert into MTRUser.mtr_files (mtd_id,mtr_portfolio_id,text,url,format,tags,file_size,value) values ('3818','4709558','results_COD_P4709558_23-04-2025_11_30_08.pdf','https://storage.sbg.cloud.ovh.net/v1/AUTH_3b171620e76e4af496c5fd050759c9f0/media.fotonower.com/results_COD_P4709558_23-04-2025_11_30_08.pdf','pdf','','0.48','0.009511382621534484') time spend for datou_step_exec : 18.17542600631714 time spend to save output : 4.5299530029296875e-05 total time spend for step 8 : 18.175471305847168 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 8 output : fin du test de rym_consolidate ############################### TEST generate_new_image_add_crop ################################ test_generate_new_image_add_crop Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : generate_new_image list_input_json : [] origin We have 1 , BBBFFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 3 ; length of list_pids : 3 ; length of list_args : 3 time to download the photos : 0.16088008880615234 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:generate_new_image Wed Apr 23 11:30:27 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4789595 ORDER BY ph.size desc We have 1 , we need 3 photos there is already 3 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 3 photo needed from our local_cache list of photo_id_missing : [] batch 1 Loaded 2 chid ids of type : 4021 ++we need 1 photos there is already 1 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 1 photo needed from our local_cache list of photo_id_missing : [] begin to treate photo :1057314774 add chi : 2208326712 , rotate : 101 (750, 750) (66, 614, 127, 456) (329, 548, 3) (329, 548) (750, 750, 3) time for calcul the mask position with numpy : 0.002470731735229492 nb_pixel_total : 98281 time to create 1 rle with old method : 0.2554914951324463 batch 1 Loaded 0 chid ids of type : 0 time for calcul the mask position with numpy : 0.0026519298553466797 nb_pixel_total : 98281 time to create 1 rle with old method : 0.2649664878845215 begin to treate photo :1057314768 add chi : 2208326711 , rotate : 18 (806, 806) (77, 390, 4, 460) (456, 313, 3) (456, 313) (806, 806, 3) time for calcul the mask position with numpy : 0.0017588138580322266 nb_pixel_total : 65372 time to create 1 rle with old method : 0.18866753578186035 batch 1 Loaded 2 chid ids of type : 4021 ++time for calcul the mask position with numpy : 0.0019266605377197266 nb_pixel_total : 80840 time to create 1 rle with old method : 0.23994874954223633 time for calcul the mask position with numpy : 0.0021142959594726562 nb_pixel_total : 65372 time to create 1 rle with old method : 0.15700531005859375 time for calcul the mask position with numpy : 0.0015060901641845703 nb_pixel_total : 8755 time to create 1 rle with old method : 0.022086381912231445 begin to treate photo :1057314766 add chi : 2208326712 , rotate : 53 (896, 896) (115, 653, 221, 622) (401, 538, 3) (401, 538) (896, 896, 3) time for calcul the mask position with numpy : 0.0018949508666992188 nb_pixel_total : 98290 time to create 1 rle with old method : 0.2669196128845215 batch 1 Loaded 3 chid ids of type : 4021 +++time for calcul the mask position with numpy : 0.002121448516845703 nb_pixel_total : 55721 time to create 1 rle with old method : 0.12605762481689453 time for calcul the mask position with numpy : 0.0019698143005371094 nb_pixel_total : 98290 time to create 1 rle with old method : 0.22896790504455566 time for calcul the mask position with numpy : 0.0023870468139648438 nb_pixel_total : 52633 time to create 1 rle with old method : 0.12883734703063965 init cache_photo without model_param we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400631_1620143 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 1 chid ids of type : 4086 Number RLEs to save : 343 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400631_1620143 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 3 chid ids of type : 4086 Number RLEs to save : 878 TO DO : save crop sub photo not yet done ! we have 1 photo to upload uploaded to storage server : ovh folder_temporaire : temp/1745400632_1620143 we have uploaded 1 photos in the portfolio 4789106 batch 1 Loaded 3 chid ids of type : 4086 Number RLEs to save : 1328 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 5.928431749343872 time spend to save output : 0.00010275840759277344 total time spend for step 1 : 5.928534507751465 caffe_path_current : About to save ! 0 After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : None nombre de crop attendu : 8, nombre de crop obtenu : 7 fin du test de generate_new_image ERROR generate_new_image_add_crop FAILED ############################### TEST velours_tree ################################ test velours_tree - Retrieving photos to tag... query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=4837217 ORDER BY ph.size desc - Loading descriptors... Size : 512 len(descriptors) : 5 Compute structured hierarchical clustering... ward : AgglomerativeClustering(n_clusters=5) ward.labels_ : [4 3 2 1 0] Elapsed time: 0.012979507446289062 graph_id used : 1145 - Beta version, working pretty good on 11-5-16 ! fin du test de velours_tree ############################### TEST step ACP ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.015958786010742188 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Wed Apr 23 11:30:34 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 find save_photo_desc_type : 5705 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5705 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5705, 'ACP_from_type_5619_size_9', 9, 9, 'ACP_from_type_5619_size_9', None, 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 6, 15), None) time to traite the descriptors : 0.0003726482391357422 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 2.243551015853882 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 3.2818543910980225 time spend to save output : 9.489059448242188e-05 total time spend for step 1 : 3.281949281692505 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306967', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306964', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306954', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306844', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306843', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306841', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306815', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306805', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306710', None, None, None, None, None, None) ('4208', None, None, None, None, None, None, None, None) ('4208', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.014291524887084961 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.01523447036743164 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Wed Apr 23 11:30:37 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. ATTENTION : le size etait trop grand : 20, on a changé sa valeur à : 9 Sauvegarde du modèle et envoi sur OVH Attention : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 existe, son contenu risque d'être écrasé model_param file didn't exist model didn't exist , save the new model in s3 local folder to save in s3 : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 update : 1745400639.8528728 done ! 1745400640.0001428 {'files': [{'name': 'pca_model.pkl', 'size': 103314, 'last_modified': '2025-04-23T09:30:39.869750', 'hash': 'd7e2c6aa9a1ef592ffdfc4abe9c66263'}], 'directories': []} Création d'un nouveau thème de classification Le thème de classification 'ACP_from_port_5709050_type_5619_size_9' existe déjà, merci de relancer avec un nouveau nom dans les params-json. time spend for datou_step_exec : 2.575806140899658 time spend to save output : 3.8623809814453125e-05 total time spend for step 1 : 2.5758447647094727 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results object of type 'int' has no len() begin to insert list_values into mtr_datou_result : length of list_values in save_final : 10 time used for this insertion : 0.014279842376708984 save_final save missing photos in datou_result : After save, about to update current ! Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : acp list_input_json : [] origin We have 1 , we have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB time to download the photos : 0.013855695724487305 About to test input to load Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:acp Wed Apr 23 11:30:40 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Cette step permet de calculer une ACP. To do loadFromThcl(), then load ParamDescType : thcl3412 thcls : [{'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'}] thcl {'id': 3412, 'mtr_user_id': 31, 'name': 'ACP_from_port_5709050_type_5619_size_9', 'pb_hashtag_id': 0, 'live': b'\x00', 'list_hashtags': '', 'svm_portfolios_learning': '0', 'photo_hashtag_type': 4398, 'photo_desc_type': 5706, 'type_classification': 'ACP', 'hashtag_id_list': '0'} Update svm_hashtag_type_desc : 5706 FOUND : 1 Here is data_from_sql_as_vec to set the ParamDescriptorType : (5706, 'ACP_from_port_5709050_type_5619_size_9', 9, 9, 'ACP_from_port_5709050_type_5619_size_9', 'pool5', 10.0, None, None, 256, None, 0, None, 8, None, None, -1000.0, 3, datetime.datetime(2022, 6, 16, 11, 8, 14), datetime.datetime(2022, 6, 16, 11, 8, 14)) model_param file didn't exist model_name : ACP_from_port_5709050_type_5619_size_9 model_type : acp list file need : ['pca_model.pkl'] file exist in s3 : ['pca_model.pkl'] file manque in s3 : [] local folder : /data/models_weight/ACP_from_port_5709050_type_5619_size_9 /data/models_weight/ACP_from_port_5709050_type_5619_size_9/pca_model.pkl size_local : 103314 size in s3 : 103314 create time local : 2025-04-23 11:30:38 create time in s3 : 2025-04-23 09:30:39 pca_model.pkl already exist and didn't need to update model_name : ACP_from_port_5709050_type_5619_size_9 On sauvegarde les nouveaux descripteurs dans le photo desc type : 5706 time to traite the descriptors : 0.00041604042053222656 storage_type for insertDescriptorsMulti : 3 Missing photo l117 : 1069306708 Missing photo l117 : 1069306710 Missing photo l117 : 1069306805 Missing photo l117 : 1069306815 Missing photo l117 : 1069306841 Missing photo l117 : 1069306843 Missing photo l117 : 1069306844 Missing photo l117 : 1069306954 Missing photo l117 : 1069306964 Missing photo l117 : 1069306967 To insert : 1069306708 To insert : 1069306710 To insert : 1069306805 To insert : 1069306815 To insert : 1069306841 To insert : 1069306843 To insert : 1069306844 To insert : 1069306954 To insert : 1069306964 To insert : 1069306967 time to insert the descriptors : 2.6432876586914062 res : {'1069306708': b'\xc3\xbf\x00\x00\x00\x00\x08\x14\x03\x05', '1069306710': b'\x00\x00\x00\x00\x00\x00\x00"\xc2\xb3', '1069306805': b'\x00\x06\x00\x00\x00\x00\x00\xc2\x8a\x00', '1069306815': b'\x00l\x00\x00\x00\xc3\x84>\x00\x00', '1069306841': b'\x004\x00\xc3\xad@=sz\x00', '1069306843': b'\x00\x00-\x00\xc3\xb9\x00\x00\x00\x00', '1069306844': b'\x00\x00\x00\xc2\x86\x00T\x00\x00\x00', '1069306954': b'\x14\xc2\x83\xc3\xbf\x1f\x00\x00\x00\x00\x00', '1069306964': b'\x06\xc3\xbf\x00X\x1a\x00\x00\x00\x1f', '1069306967': b'\x00\x00\x00\x00\x00\x00\xc2\xab\x00\x00'} time spend for datou_step_exec : 7.186103105545044 time spend to save output : 8.058547973632812e-05 total time spend for step 1 : 7.18618369102478 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : acp we use saveGeneral [1069306967, 1069306964, 1069306954, 1069306844, 1069306843, 1069306841, 1069306815, 1069306805, 1069306710, 1069306708] Looping around the photos to save general results len do output : 10 /1069306708Didn't retrieve data . /1069306710Didn't retrieve data . /1069306805Didn't retrieve data . /1069306815Didn't retrieve data . /1069306841Didn't retrieve data . /1069306843Didn't retrieve data . /1069306844Didn't retrieve data . /1069306954Didn't retrieve data . /1069306964Didn't retrieve data . /1069306967Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306967', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306964', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306954', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306844', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306843', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306841', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306815', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306805', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306710', None, None, None, None, None, None) ('4212', None, None, None, None, None, None, None, None) ('4212', '5709050', '1069306708', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 20 time used for this insertion : 0.01437520980834961 save_final save missing photos in datou_result : After save, about to update current ! fin du test de la step acp ############################### TEST blur_crop ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! List Step Type Loaded in datou : crop_condition list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.19316792488098145 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 1 step1:crop_condition Wed Apr 23 11:30:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Loading chi in step crop with photo_hashtag_type : 4356 Loading chi in step crop for list_pids : 2 ! batch 1 Loaded 3 chid ids of type : 4356 +++++ begin to crop the class : Papier_Magazine param for this class : {} filtre for class : Papier_Magazine hashtag_id of this class : 2107752386 begin to crop the class : carton_brun param for this class : {} filtre for class : carton_brun hashtag_id of this class : 2107753024 begin to crop the class : carton_gris param for this class : {} filtre for class : carton_gris hashtag_id of this class : 2107753020 begin to crop the class : cartonnette param for this class : {} filtre for class : cartonnette hashtag_id of this class : 702398920 begin to crop the class : kraft param for this class : {} filtre for class : kraft hashtag_id of this class : 493202403 begin to crop the class : autre_refus param for this class : {} filtre for class : autre_refus hashtag_id of this class : 2107752406 begin to crop the class : metal param for this class : {} filtre for class : metal hashtag_id of this class : 492628673 begin to crop the class : plastique param for this class : {} filtre for class : plastique hashtag_id of this class : 492725882 begin to crop the class : teint_dans_la_masse param for this class : {} filtre for class : teint_dans_la_masse hashtag_id of this class : 2107752385 begin to crop the class : environnement param for this class : {} filtre for class : environnement hashtag_id of this class : 493012381 begin to crop the class : contaminant param for this class : {} filtre for class : contaminant hashtag_id of this class : 681467679 map_result returned by crop_photo_return_map_crop : length : 3 About to insert : list_path_to_insert length 0 new photo from crops ! About to upload 0 photos WARNING : list_path_to_insert is empty, cannot upload ! we have finished the crop for the class : contaminant time spend for datou_step_exec : 0.14570307731628418 time spend to save output : 0.00010132789611816406 total time spend for step 1 : 0.14580440521240234 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : crop_condition we use saveGeneral [1105701500, 1105701516] Looping around the photos to save general results len do output : 3 /1105703688Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703689Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . /1105703686Didn't retrieve data .Didn't retrieve data .Didn't retrieve data . before output type Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701500', None, None, None, None, None, None) ('3990', None, None, None, None, None, None, None, None) ('3990', '6135916', '1105701516', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 11 time used for this insertion : 0.015141010284423828 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 1 output : {1105703688: [1105701500, 'temp/1745400647_1620143_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436373819_1.jpg', (108, 300, 16, 138)], 1105703689: [1105701500, 'temp/1745400647_1620143_1105701500_b57a1caec2d74ede6814095fdd28cb27_polygon_blur_2436374262_1.jpg', (47, 300, 91, 247)], 1105703686: [1105701516, 'temp/1745400647_1620143_1105701516_047b0ce16fe5e308d8512c83125c4058_polygon_blur_2436374092_1.jpg', (25, 175, 137, 235)]} fin du test de la step crop option blur ############################### TEST pma_consolidate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12666 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12667 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12664 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12664 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12671 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 12666 have datatype=11 whereas input 0 of step 12664 have datatype=2 WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 3 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12667 doesn't seem to be define in the database( WARNING : type of input 1 of step 12664 doesn't seem to be define in the database( WARNING : type of input 1 of step 12671 doesn't seem to be define in the database( WARNING : output 1 of step 12664 have datatype=7 whereas input 1 of step 12671 have datatype=None WARNING : type of output 1 of step 12671 doesn't seem to be define in the database( WARNING : type of input 4 of step 12665 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 12666 doesn't seem to be define in the database( WARNING : type of input 1 of step 12667 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio, final list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.30538201332092285 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 5 step1:copy_chis Wed Apr 23 11:30:47 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.057695865631103516 time spend to save output : 5.125999450683594e-05 total time spend for step 1 : 0.05774712562561035 step2:consolidate_hashtags_from_manual_portfolio Wed Apr 23 11:30:48 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 on est dans le IF portfolio mere 26T SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9778120 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) AND mptpi.`min_score`=0.1 To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 1.8561546802520752 time spend to save output : 8.606910705566406e-05 total time spend for step 2 : 1.8562407493591309 step3:rle_unique_nms_with_priority Wed Apr 23 11:30:49 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.0517120361328125 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.033884286880493164 map_output_result : {1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904), 1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064)} End step rle-unique-nms time spend for datou_step_exec : 0.8526575565338135 time spend to save output : 8.535385131835938e-05 total time spend for step 3 : 0.8527429103851318 step4:ventilate_hashtags_in_portfolio Wed Apr 23 11:30:50 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 0.43953371047973633 time spend to save output : 0.0002090930938720703 total time spend for step 4 : 0.4397428035736084 step5:final Wed Apr 23 11:30:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! complete output_args for input 2 VR 22-3-18 : For now we do not clean correctly the datou structure Beginning of datou step final ! time spend for datou_step_exec : 0.018581628799438477 time spend to save output : 2.765655517578125e-05 total time spend for step 5 : 0.018609285354614258 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False original output for save of step final : {1114046377: ('0.3133414848207032',), 1114046597: ('0.3133414848207032',)} new output for save of step final : {1114046377: ('0.3133414848207032',), 1114046597: ('0.3133414848207032',)} [1114046377, 1114046597] Looping around the photos to save general results len do output : 2 /1114046377.Didn't retrieve data . /1114046597.Didn't retrieve data . before output type Used above Used above Managing all output in save final without adding information in the mtr_datou_result ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046377', None, None, None, None, None, None) ('4492', None, None, None, None, None, None, None, None) ('4492', '6549724', '1114046597', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 6 time used for this insertion : 0.015088319778442383 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 5 output : {1114046377: ('0.3133414848207032',), 1114046597: ('0.3133414848207032',)} fin du test de portfolio mere absolue dans consolidate ############################### TEST pma_ventilate ################################ Inside batchDatouExec : verbose : False # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 12795 copy_chis is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 12796 consolidate_hashtags_from_manual_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 12793 rle_unique_nms_with_priority is not consistent : 3 used against 1 in the step definition ! WARNING : number of outputs for step 12793 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : output 0 of step 12795 have datatype=11 whereas input 0 of step 12793 have datatype=2 WARNING : type of output 1 of step 12796 doesn't seem to be define in the database( WARNING : type of input 1 of step 12793 doesn't seem to be define in the database( WARNING : type of input 1 of step 12800 doesn't seem to be define in the database( WARNING : output 1 of step 12793 have datatype=7 whereas input 1 of step 12800 have datatype=None WARNING : type of output 1 of step 12795 doesn't seem to be define in the database( WARNING : type of input 1 of step 12796 doesn't seem to be define in the database( DataTypes for each output/input checked ! List Step Type Loaded in datou : copy_chis, consolidate_hashtags_from_manual_portfolio, rle_unique_nms_with_priority, ventilate_hashtags_in_portfolio list_input_json : [] origin We have 1 , BBFFwe have missing 0 photos in the step downloads : photo missing : [] try to delete the photos missing in DB length of list_filenames : 2 ; length of list_pids : 2 ; length of list_args : 2 time to download the photos : 0.2803215980529785 About to test input to load we should then remove the video here, and this would fix the bug of datou_current ! Calling datou_exec Inside datou_exec : verbose : False number of steps : 4 step1:copy_chis Wed Apr 23 11:30:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Begin step datou_step_copy_crop batch 1 Loaded 21 chid ids of type : 4482 batch 1 Loaded 88 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! time spend for datou_step_exec : 0.05638408660888672 time spend to save output : 6.604194641113281e-05 total time spend for step 1 : 0.05645012855529785 step2:consolidate_hashtags_from_manual_portfolio Wed Apr 23 11:30:51 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step consolidate_hashtags_from_manual_portfolio Iterating over portfolio : 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement')) To do TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755323 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755324 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755325 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755326 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755327 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755328 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 53 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755329 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755330 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755331 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755332 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755333 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755334 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755335 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755336 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755337 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755338 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755339 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755340 AND mpp.hide_status=0 ORDER BY ph.size desc TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755341 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 48 chid ids of type : 4482 begin to find the sub_photo_id : TODO : # On doit donc construire les chi a partir des informations dans les photos filles query : SELECT ph.photo_id FROM MTRBack.photos ph, MTRUser.mtr_portfolio_photos mpp WHERE ph.photo_id=mpp.mtr_photo_id AND mpp.mtr_portfolio_id=9755342 AND mpp.hide_status=0 ORDER BY ph.size desc batch 1 Loaded 101 chid ids of type : 4482 begin to find the sub_photo_id : begin to find the sub_photo_id : To test ! Use context local managing function ! time spend for datou_step_exec : 1.581054925918579 time spend to save output : 2.6464462280273438e-05 total time spend for step 2 : 1.5810813903808594 step3:rle_unique_nms_with_priority Wed Apr 23 11:30:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed complete output_args for input 0 We expect there is only one output and this part is used while all output are not tuple or array We expect there is only one output and this part is used while all output are not tuple or array VR 22-3-18 : For now we do not clean correctly the datou structure Begin step rle-unique-nms batch 1 Loaded 88 chid ids of type : 4490 seulement à utiliser dans la step consolidation batch 1 Loaded 50 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.036257266998291016 seulement à utiliser dans la step consolidation batch 1 Loaded 38 chid ids of type : 4490 Number RLEs to save : 0 TO DO : save crop sub photo not yet done ! save time : 0.0329287052154541 map_output_result : {1114046377: (0.09264291817443555, 'Should be the crop_list due to order', 0.14304832271019904), 1114046597: (0.09264291817443555, 'Should be the crop_list due to order', 0.042237513638672064)} End step rle-unique-nms time spend for datou_step_exec : 0.7275576591491699 time spend to save output : 7.534027099609375e-05 total time spend for step 3 : 0.727632999420166 step4:ventilate_hashtags_in_portfolio Wed Apr 23 11:30:53 2025 VR 17-11-17 : now, only for linear exec dependencies tree, some output goes to fill the input of the next VR 22-3-18 : now we test the dependencies tree, but keep two separate code for datou_prepare_output_input until the code is correctly tested, clean and works in both case VR 22-3-18 : but we use the first code for the first step id = -1, build in the code of datou_exec VR 22-3-18 : we should manage here the case when we are at the first step instead of building this step before datou_exec Currently we do not manage missing dependencies information, that could maybe be correctly interpreted with default behavior Some of the step done at execution of the step could be done before when the tree of execution is build and the dependencies of different step analysed We should have FATAL ERROR but same_nb_input_output==True : this should be an optionnal input ! VR 22-3-18 : For now we do not clean correctly the datou structure beginning of datou step ventilate_hashtags_in_portfolio : To implement ! Iterating over portfolio : 6549724 get user id for portfolio 6549724 SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=9974548 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4490 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do To do ! Use context local managing function ! SELECT mptpi.id, mptpi.mtr_portfolio_id_1, mptpi.mtr_portfolio_id_2, mptpi.type, mptpi.hashtag_id, mptpi.min_score, mptpi.mtr_user_id, mptpi.created_at, mptpi.updated_at, mptpi.last_updated_at_desc, mptpi.last_updated_at_asc, h.hashtag FROM MTRPhoto.mtr_port_to_port_ids mptpi, MTRBack.hashtags h WHERE h.hashtag_id=mptpi.hashtag_id AND mptpi.`mtr_portfolio_id_1`=6549724 AND mptpi.`type`=4483 AND mptpi.`hashtag_id` in (select hashtag_id FROM MTRBack.hashtags where hashtag in ('papier','carton','metal','pet_clair','pehd','pet_fonce','pet_opaque','barquette_opaque','film_plastique','ela','sac','textiles','verre','organique','dasri','masque','encombrant','autre_emballage','autre_non_emballage','environnement','mal_croppe','flou')) AND mptpi.`min_score`=0.1 To do lien utilise dans velours : https://www.fotonower.com/velours/9755323,9755324,9755325,9755326,9755327,9755328,9755329,9755330,9755331,9755332,9755333,9755334,9755335,9755336,9755337,9755338,9755339,9755340,9755341,9755342,9755344,9755345?tags=papier,carton,metal,pet_clair,pehd,pet_fonce,pet_opaque,barquette_opaque,film_plastique,ela,sac,textiles,verre,organique,dasri,masque,encombrant,autre_emballage,autre_non_emballage,environnement,mal_croppe,flou&datou_id_consolidate=4387&port_consolidate=6549724 time spend for datou_step_exec : 0.8973917961120605 time spend to save output : 6.556510925292969e-05 total time spend for step 4 : 0.8974573612213135 caffe_path_current : About to save ! 1 Inside saveOutput : final : True verbose : False saveOutput not yet implemented for datou_step.type : ventilate_hashtags_in_portfolio we use saveGeneral [1114046377, 1114046597] Looping around the photos to save general results len do output : 1 /6549724. before output type Here is an output not treated by saveGeneral : Managing all output in save final without adding information in the mtr_datou_result ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046377', None, None, None, None, None, None) ('4553', None, None, None, None, None, None, None, None) ('4553', '6549724', '1114046597', None, None, None, None, None, None) begin to insert list_values into mtr_datou_result : length of list_values in save_final : 3 time used for this insertion : 0.015015363693237305 save_final save missing photos in datou_result : After save, about to update current ! datou_cur_ids : [] len(datou.list_steps) : 4 output : {6549724: [{'papier': 9755346, 'carton': 9755347, 'metal': 9755348, 'pet_clair': 9755349, 'pehd': 9755350, 'pet_fonce': 9755351, 'pet_opaque': 9755352, 'barquette_opaque': 9755353, 'film_plastique': 9755354, 'ela': 9755355, 'sac': 9755356, 'textiles': 9755357, 'verre': 9755358, 'organique': 9755359, 'dasri': 9755360, 'masque': 9755361, 'encombrant': 9755362, 'autre_emballage': 9755363, 'autre_non_emballage': 9755364, 'environnement': 9755365, 'mal_croppe': 9755366, 'flou': 9755367}]} fin du test de portfolio mere absolue dans consolidate #&_#_#&_# TEST sam SUCCEEDED #&_#_#&_# #&_#_#&_# TEST frcnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST thcl SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tfhub2 SUCCEEDED #&_#_#&_# #&_#_#&_# TEST ordonner SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST data_augmentation_ellipse_varroa_tile_rotate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST flip SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_rles FAILED #&_#_#&_# #&_#_#&_# TEST angular_coeff SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detection_filter_by_classif SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST detect_point_224x224 SUCCEEDED #&_#_#&_# #&_#_#&_# TEST certificat_qualite_papier SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_temperature_detection SUCCEEDED #&_#_#&_# #&_#_#&_# TEST broca SUCCEEDED #&_#_#&_# #&_#_#&_# TEST crop_conditional SUCCEEDED #&_#_#&_# #&_#_#&_# TEST image_blanchir SUCCEEDED #&_#_#&_# #&_#_#&_# TEST darker_image SUCCEEDED #&_#_#&_# #&_#_#&_# TEST img_aug SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_split_dark SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_append SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_horaire FAILED #&_#_#&_# #&_#_#&_# TEST rle_unique_nms_with_priority SUCCEEDED #&_#_#&_# #&_#_#&_# TEST random_deformation SUCCEEDED #&_#_#&_# #&_#_#&_# TEST tile SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rotate_chi SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_pet_clair_0121_no_cnn SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rubbia_carac_jrm_no_mask_detect SUCCEEDED #&_#_#&_# #&_#_#&_# TEST ventilate_hashtags_in_portfolio SUCCEEDED #&_#_#&_# #&_#_#&_# TEST poly_ro_rle SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_sts SUCCEEDED #&_#_#&_# #&_#_#&_# TEST cod_download SUCCEEDED #&_#_#&_# #&_#_#&_# TEST sendgrid SUCCEEDED #&_#_#&_# #&_#_#&_# TEST rym_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST generate_new_image_add_crop FAILED #&_#_#&_# #&_#_#&_# TEST velours_tree SUCCEEDED #&_#_#&_# #&_#_#&_# TEST step ACP SUCCEEDED #&_#_#&_# #&_#_#&_# TEST blur_crop SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_consolidate SUCCEEDED #&_#_#&_# #&_#_#&_# TEST pma_ventilate SUCCEEDED #&_#_#&_# #&_# TEST FAILED #&_# : tests/datou_test #&_# #&_# END OF TEST #&_# : tests/datou_test #&_# #&_# BEGIN OF TEST : mtr/database_queries/CacheModelData_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py Test Cache Model Data test a faire VR 27-9-17 #&_# TEST SUCCEEDED #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/CacheModelData_queries #&_# #&_# BEGIN OF TEST : tests/cache_photo_data_test #&_# /home/admin/workarea/git/Velours/python/tests/cache_photo_data_test.py Test local_cache_photo ############################### test_download_photos_by_local_cache ################################ test download portfolio 1162416 : 574 photos We have 1 , we need 574 photos there is already 574 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 574 photo needed from our local_cache list of photo_id_missing : [] test download a list if photos : 10 photos (6 exist in ovh and 4 missing in ovh) we need 10 photos there is already 6 photos exist in our local_cache we have to download 4 photos download_photo : 1 to 2000 BBBBHTTP Error 404: Not Found can't download the photo : 1109585109 FHTTP Error 404: Not Found can't download the photo : 1109585436 FHTTP Error 404: Not Found can't download the photo : 1109585121 FHTTP Error 404: Not Found can't download the photo : 1109585120 Fwe have successful downloaded 0 photos there are 4 photos missing finally, we can get 6 photo needed from our local_cache list of photo_id_missing : [1109585109, 1109585436, 1109585121, 1109585120] ############################### test_update_time_created ################################ we need 1 photos there is already 1 photos exist in our local_cache we have to download 0 photos we have successful downloaded 0 photos there are 0 photos missing finally, we can get 1 photo needed from our local_cache list of photo_id_missing : [] #&_#_#&_# TEST cache photo data SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : tests/cache_photo_data_test #&_# #&_# END OF TEST #&_# : tests/cache_photo_data_test #&_# #&_# BEGIN OF TEST : mtr/mask_rcnn/prepare_maskdata #&_# /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py test prepare mask data 2096875719 599722655 batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 16 chid ids of type : 840 +++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++++++++++batch 1 Loaded 19 chid ids of type : 840 ++++++++++batch 1 Loaded 20 chid ids of type : 840 ++++++++++++{2096875719: 'Plaque-immatriculation', 599722655: 'capot'} logo-marque 2096875717 907850592 x0 : 38 y1 : 269 width : 32, height : 38, area : 1216, score : 1.0 None pare-choc 624624117 907850592 x0 : 0 y1 : 559 width : 362, height : 235, area : 85070, score : 1.0 None coffre 495920967 907850592 x0 : 0 y1 : 398 width : 302, height : 336, area : 101472, score : 1.0 None coffre 495920967 907850592 x0 : 626 y1 : 206 width : 18, height : 35, area : 630, score : 1.0 None aile-arriere 2106233861 907850592 x0 : 189 y1 : 476 width : 282, height : 235, area : 66270, score : 1.0 None roue 492689227 907850592 x0 : 275 y1 : 620 width : 170, height : 215, area : 36550, score : 1.0 None Plaque-immatriculation 2096875719 907850592 x0 : 10 y1 : 454 width : 108, height : 84, area : 9072, score : 1.0 None feu-arriere 2096875713 907850592 x0 : 192 y1 : 359 width : 147, height : 96, area : 14112, score : 1.0 None poignee 499500794 907850592 x0 : 437 y1 : 315 width : 42, height : 37, area : 1554, score : 1.0 None poignee 499500794 907850592 x0 : 567 y1 : 282 width : 25, height : 31, area : 775, score : 1.0 None Pot-echappement 2096875720 907850592 x0 : 155 y1 : 532 width : 21, height : 20, area : 420, score : 1.0 None toit 492731002 907850592 x0 : 136 y1 : 74 width : 281, height : 21, area : 5901, score : 1.0 None Pare-brise 2096875709 907850592 x0 : 1 y1 : 241 width : 285, height : 165, area : 47025, score : 1.0 None porte 492654799 907850592 x0 : 354 y1 : 442 width : 210, height : 380, area : 79800, score : 1.0 None porte 492654799 907850592 x0 : 501 y1 : 389 width : 135, height : 324, area : 43740, score : 1.0 None Info-modele 2096875721 907850592 x0 : 132 y1 : 295 width : 53, height : 31, area : 1643, score : 1.0 None vitre 492925064 907850592 x0 : 407 y1 : 201 width : 123, height : 122, area : 15006, score : 1.0 None vitre 492925064 907850592 x0 : 377 y1 : 201 width : 64, height : 118, area : 7552, score : 1.0 None vitre 492925064 907850592 x0 : 522 y1 : 203 width : 102, height : 120, area : 12240, score : 1.0 None Essuie-glace 2096875722 907850592 x0 : 7 y1 : 250 width : 72, height : 61, area : 4392, score : 1.0 None /data/data_root/test_preparedata/train/907850592.jpg logo-marque 2096875717 907862724 x0 : 100 y1 : 282 width : 35, height : 41, area : 1435, score : 1.0 None pare-choc 624624117 907862724 x0 : 1 y1 : 483 width : 435, height : 194, area : 84390, score : 1.0 None aile-arriere 2106233861 907862724 x0 : 399 y1 : 411 width : 110, height : 128, area : 14080, score : 1.0 None retroviseur 492844413 907862724 x0 : 600 y1 : 203 width : 41, height : 37, area : 1517, score : 1.0 None coffre 495920967 907862724 x0 : 5 y1 : 351 width : 369, height : 270, area : 99630, score : 1.0 None Cache-reservoir 2096875718 907862724 x0 : 417 y1 : 289 width : 50, height : 61, area : 3050, score : 1.0 None roue 492689227 907862724 x0 : 367 y1 : 541 width : 121, height : 190, area : 22990, score : 1.0 None poignee 499500794 907862724 x0 : 478 y1 : 275 width : 33, height : 25, area : 825, score : 1.0 None toit 492731002 907862724 x0 : 226 y1 : 110 width : 223, height : 36, area : 8028, score : 1.0 None feu-arriere 2096875713 907862724 x0 : 294 y1 : 331 width : 112, height : 146, area : 16352, score : 1.0 None poignee 499500794 907862724 x0 : 566 y1 : 252 width : 20, height : 27, area : 540, score : 1.0 None Plaque-immatriculation 2096875719 907862724 x0 : 55 y1 : 436 width : 142, height : 54, area : 7668, score : 1.0 None Pare-brise 2096875709 907862724 x0 : 6 y1 : 225 width : 366, height : 132, area : 48312, score : 1.0 None porte 492654799 907862724 x0 : 395 y1 : 391 width : 170, height : 311, area : 52870, score : 1.0 None porte 492654799 907862724 x0 : 508 y1 : 354 width : 101, height : 269, area : 27169, score : 1.0 None vitre 492925064 907862724 x0 : 451 y1 : 202 width : 85, height : 99, area : 8415, score : 1.0 None vitre 492925064 907862724 x0 : 524 y1 : 202 width : 70, height : 99, area : 6930, score : 1.0 None vitre 492925064 907862724 x0 : 428 y1 : 208 width : 50, height : 102, area : 5100, score : 1.0 None Essuie-glace 2096875722 907862724 x0 : 114 y1 : 218 width : 186, height : 27, area : 5022, score : 1.0 None Info-modele 2096875721 907862724 x0 : 10 y1 : 306 width : 41, height : 33, area : 1353, score : 1.0 None /data/data_root/test_preparedata/train/907862724.jpg porte 492654799 907863602 x0 : 129 y1 : 380 width : 300, height : 279, area : 83700, score : 1.0 None porte 492654799 907863602 x0 : 193 y1 : 345 width : 228, height : 215, area : 49020, score : 1.0 None porte 492654799 907863602 x0 : 379 y1 : 385 width : 257, height : 286, area : 73502, score : 1.0 None porte 492654799 907863602 x0 : 400 y1 : 365 width : 195, height : 248, area : 48360, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 419 width : 124, height : 148, area : 18352, score : 1.0 None roue 492689227 907863602 x0 : 0 y1 : 411 width : 127, height : 129, area : 16383, score : 1.0 None roue 492689227 907863602 x0 : 580 y1 : 449 width : 60, height : 129, area : 7740, score : 1.0 None roue 492689227 907863602 x0 : 584 y1 : 444 width : 50, height : 126, area : 6300, score : 1.0 None retroviseur 492844413 907863602 x0 : 165 y1 : 225 width : 53, height : 51, area : 2703, score : 1.0 None vitre 492925064 907863602 x0 : 211 y1 : 218 width : 197, height : 109, area : 21473, score : 1.0 None vitre 492925064 907863602 x0 : 217 y1 : 214 width : 193, height : 99, area : 19107, score : 1.0 None vitre 492925064 907863602 x0 : 409 y1 : 214 width : 188, height : 98, area : 18424, score : 1.0 None vitre 492925064 907863602 x0 : 414 y1 : 206 width : 187, height : 96, area : 17952, score : 1.0 None poignee 499500794 907863602 x0 : 320 y1 : 271 width : 46, height : 19, area : 874, score : 1.0 None poignee 499500794 907863602 x0 : 321 y1 : 270 width : 44, height : 19, area : 836, score : 1.0 None poignee 499500794 907863602 x0 : 553 y1 : 270 width : 62, height : 25, area : 1550, score : 1.0 None poignee 499500794 907863602 x0 : 565 y1 : 266 width : 42, height : 16, area : 672, score : 1.0 None capot 599722655 907863602 x0 : -1 y1 : 215 width : 135, height : 37, area : 4995, score : 1.0 None capot 599722655 907863602 x0 : 242 y1 : 123 width : 330, height : 52, area : 17160, score : 1.0 None /data/data_root/test_preparedata/train/907863602.jpg porte 492654799 907863940 x0 : 109 y1 : 426 width : 283, height : 265, area : 74995, score : 1.0 None porte 492654799 907863940 x0 : 179 y1 : 399 width : 203, height : 221, area : 44863, score : 1.0 None porte 492654799 907863940 x0 : 354 y1 : 425 width : 277, height : 266, area : 73682, score : 1.0 None porte 492654799 907863940 x0 : 370 y1 : 407 width : 179, height : 233, area : 41707, score : 1.0 None roue 492689227 907863940 x0 : -1 y1 : 463 width : 98, height : 141, area : 13818, score : 1.0 None roue 492689227 907863940 x0 : 0 y1 : 468 width : 88, height : 147, area : 12936, score : 1.0 None roue 492689227 907863940 x0 : 523 y1 : 493 width : 117, height : 146, area : 17082, score : 1.0 None roue 492689227 907863940 x0 : 525 y1 : 493 width : 111, height : 147, area : 16317, score : 1.0 None retroviseur 492844413 907863940 x0 : 138 y1 : 278 width : 46, height : 44, area : 2024, score : 1.0 None vitre 492925064 907863940 x0 : 182 y1 : 266 width : 200, height : 95, area : 19000, score : 1.0 None vitre 492925064 907863940 x0 : 385 y1 : 257 width : 172, height : 90, area : 15480, score : 1.0 None vitre 492925064 907863940 x0 : 394 y1 : 256 width : 144, height : 86, area : 12384, score : 1.0 None vitre 492925064 907863940 x0 : 549 y1 : 253 width : 58, height : 53, area : 3074, score : 1.0 None poignee 499500794 907863940 x0 : 300 y1 : 306 width : 59, height : 17, area : 1003, score : 1.0 None poignee 499500794 907863940 x0 : 307 y1 : 313 width : 51, height : 24, area : 1224, score : 1.0 None poignee 499500794 907863940 x0 : 527 y1 : 308 width : 53, height : 22, area : 1166, score : 1.0 None poignee 499500794 907863940 x0 : 536 y1 : 310 width : 45, height : 24, area : 1080, score : 1.0 None capot 599722655 907863940 x0 : 219 y1 : 181 width : 370, height : 38, area : 14060, score : 1.0 None logo-roue 2106233859 907863940 x0 : 7 y1 : 419 width : 27, height : 43, area : 1161, score : 1.0 None logo-roue 2106233859 907863940 x0 : 594 y1 : 441 width : 26, height : 25, area : 650, score : 1.0 None /data/data_root/test_preparedata/val/907863940.jpg #&_#_#&_# TEST prepare mask data poly SUCCEEDED #&_#_#&_# 2107755846 batch 1 Loaded 15 chid ids of type : 2622 {2107755846: 'pet_clair'} environment 492622729 964453879 x0 : 0 y1 : 479 width : 112, height : 415, area : 46480, score : 1.0 None error 501120777 964453879 x0 : 142 y1 : 479 width : 28, height : 20, area : 560, score : 1.0 None error 501120777 964453879 x0 : 151 y1 : 99 width : 23, height : 42, area : 966, score : 1.0 None error 501120777 964453879 x0 : 175 y1 : 59 width : 117, height : 59, area : 6903, score : 1.0 None error 501120777 964453879 x0 : 282 y1 : 479 width : 35, height : 62, area : 2170, score : 1.0 None error 501120777 964453879 x0 : 315 y1 : 460 width : 19, height : 26, area : 494, score : 1.0 None error 501120777 964453879 x0 : 353 y1 : 361 width : 32, height : 41, area : 1312, score : 1.0 None error 501120777 964453879 x0 : 403 y1 : 215 width : 47, height : 51, area : 2397, score : 1.0 None error 501120777 964453879 x0 : 497 y1 : 253 width : 89, height : 55, area : 4895, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 58, height : 68, area : 3944, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 20, height : 32, area : 640, score : 1.0 None error 501120777 964453879 x0 : 638 y1 : 225 width : 17, height : 27, area : 459, score : 1.0 None pet_clair 2107755846 964453879 x0 : 0 y1 : 479 width : 719, height : 479, area : 344401, score : 1.0 None error 501120777 964453879 x0 : 546 y1 : 195 width : 60, height : 68, area : 4080, score : 1.0 None error 501120777 964453879 x0 : 613 y1 : 323 width : 22, height : 32, area : 704, score : 1.0 None /data/data_root/test_preparedata/val/964453879.jpg #&_#_#&_# TEST prepare mask data rle SUCCEEDED #&_#_#&_# time for calcul the mask position with numpy : 0.0001823902130126953 nb_pixel_total : 6 time to create 1 rle with old method : 5.9604644775390625e-05 Sanity check PASSED : sum_rle_size : 12 height * width : 12 7 #&_#_#&_# TEST prepare mask data mat SUCCEEDED #&_#_#&_# #&_# TEST SUCCEEDED #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# END OF TEST #&_# : mtr/mask_rcnn/prepare_maskdata #&_# #&_# BEGIN OF TEST : mtr/database_queries/portfolio_queries #&_# /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py test portfolio queries Catched exception ! Connect or reconnect ! #&_# TEST SUCCEEDED #&_# : mtr/database_queries/portfolio_queries #&_# #&_# END OF TEST #&_# : mtr/database_queries/portfolio_queries #&_# #&_# BEGIN OF TEST : prod/memo/memo #&_# /home/admin/workarea/git/Velours/python/prod/memo/memo.py SLA Mensuel python version used : 3 ############################### TEST memo ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_2' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! Error parsing crontab ! [Errno 2] No such file or directory: '' You better check your LOGRASPI env !!! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_2" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,4211,4200,4205,3594,3327) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0001728785023265608, 'nb_photo': 18, 'list_port_cont': '6861407,6862123,6862547,6867908,6868674,6869389,6869558,6877707,6877972,6881690,6891352,6894202,6907047,6922714,6925593', 'assoc_port': '6790887:6862547,6794193:6862123,6834822:6877972,6840554:6877707,6845438:6869389,6846972:6869558,6851003:6868674,6853215:6861407,6864848:6867908,6881492:6881690,6889190:6891352,6894094:6894202,6906036:6907047,6921897:6922714,6925484:6925593', 'assoc_mat': '6790887:barquette_opaque:4207:6862547,6794193:barquette_opaque:4207:6862123,6834822:barquette_opaque:4207:6877972,6840554:barquette_opaque:4207:6877707,6845438:barquette_opaque:4207:6869389,6846972:barquette_opaque:4207:6869558,6851003:barquette_opaque:4207:6868674,6853215:barquette_opaque:4207:6861407,6864848:barquette_opaque:4207:6867908,6881492:barquette_opaque:4207:6881690,6889190:barquette_opaque:4207:6891352,6894094:barquette_opaque:4207:6894202,6906036:barquette_opaque:4207:6907047,6921897:barquette_opaque:4207:6922714,6925484:barquette_opaque:4207:6925593'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 8.469862336652651e-05, 'nb_photo': 44, 'list_port_cont': '6626450,6627857,6628405,6628829,6644425,6664932,6665236,6666075,6666652,6669243,6669771,6706891,6712605,6719637,6720391,6720629,6722154,6744912,6762879,6775287,6835808,6841133,6842925,6846016,6852775,6853230', 'assoc_port': '6625827:6626450,6627447:6627857,6627801:6628405,6628455:6628829,6630580:6664932,6630581:6665236,6630822:6666075,6631618:6669771,6639123:6644425,6665718:6666652,6668655:6669243,6706262:6706891,6708643:6712605,6719441:6719637,6719951:6720391,6720202:6720629,6721827:6722154,6744086:6744912,6762224:6762879,6774283:6775287,6834821:6835808,6840554:6841133,6842390:6842925,6845438:6846016,6852117:6852775,6852533:6853230', 'assoc_mat': '6625827:barquette_opaque:4209:6626450,6627447:barquette_opaque:4209:6627857,6627801:barquette_opaque:4209:6628405,6628455:barquette_opaque:4209:6628829,6630580:barquette_opaque:4209:6664932,6630581:barquette_opaque:4209:6665236,6630822:barquette_opaque:4209:6666075,6631618:barquette_opaque:4209:6669771,6639123:barquette_opaque:4209:6644425,6665718:barquette_opaque:4209:6666652,6668655:barquette_opaque:4209:6669243,6706262:barquette_opaque:4209:6706891,6708643:barquette_opaque:4209:6712605,6719441:barquette_opaque:4209:6719637,6719951:barquette_opaque:4209:6720391,6720202:barquette_opaque:4209:6720629,6721827:barquette_opaque:4209:6722154,6744086:barquette_opaque:4209:6744912,6762224:barquette_opaque:4209:6762879,6774283:barquette_opaque:4209:6775287,6834821:barquette_opaque:4209:6835808,6840554:barquette_opaque:4209:6841133,6842390:barquette_opaque:4209:6842925,6845438:barquette_opaque:4209:6846016,6852117:barquette_opaque:4209:6852775,6852533:barquette_opaque:4209:6853230'}, {'unwanted_material': 'carton', 'main_material': 'aluminium', 'pht_type': 4205, 'ratio': 0.01022994561299725, 'nb_photo': 16, 'list_port_cont': '6625532,6880743,6880786', 'assoc_port': '6625304:6625532,6880715:6880786,6880716:6880743', 'assoc_mat': '6625304:carton:4205:6625532,6880715:carton:4205:6880786,6880716:carton:4205:6880743'}, {'unwanted_material': 'carton', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.9683717828305286, 'nb_photo': 2565, 'list_port_cont': 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{'unwanted_material': 'papier', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.9902901831097267, 'nb_photo': 2955, 'list_port_cont': '6860598,6860874,6861066,6861173,6861345,6861539,6861959,6862604,6863422,6863593,6864917,6865058,6866086,6866174,6876761,6877571,6877655,6877751,6881620,6886395,6888025,6889756,6894811,6902822,6905423,6907081,6909151,6920947', 'assoc_port': '6860529:6861066,6860533:6861173,6860534:6860598,6860536:6860874,6861163:6861539,6861164:6861345,6861513:6861959,6861515:6862604,6863219:6863593,6863221:6863422,6864847:6866174,6864849:6865058,6864854:6864917,6865737:6866086,6876690:6876761,6877477:6877751,6877481:6877655,6877482:6877571,6881493:6881620,6886257:6886395,6887731:6888025,6889188:6889756,6894658:6894811,6899249:6909151,6900676:6902822,6903711:6907081,6905103:6905423,6920015:6920947', 'assoc_mat': '6860529:papier:4209:6861066,6860533:papier:4209:6861173,6860534:papier:4209:6860598,6860536:papier:4209:6860874,6861163:papier:4209:6861539,6861164:papier:4209:6861345,6861513:papier:4209:6861959,6861515:papier:4209:6862604,6863219:papier:4209:6863593,6863221:papier:4209:6863422,6864847:papier:4209:6866174,6864849:papier:4209:6865058,6864854:papier:4209:6864917,6865737:papier:4209:6866086,6876690:papier:4209:6876761,6877477:papier:4209:6877751,6877481:papier:4209:6877655,6877482:papier:4209:6877571,6881493:papier:4209:6881620,6886257:papier:4209:6886395,6887731:papier:4209:6888025,6889188:papier:4209:6889756,6894658:papier:4209:6894811,6899249:papier:4209:6909151,6900676:papier:4209:6902822,6903711:papier:4209:6907081,6905103:papier:4209:6905423,6920015:papier:4209:6920947'}, {'unwanted_material': 'papier', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.010245453120925422, 'nb_photo': 31, 'list_port_cont': '6647707,6861711', 'assoc_port': '6647200:6647707,6861521:6861711', 'assoc_mat': '6647200:papier:4200:6647707,6861521:papier:4200:6861711'}, {'unwanted_material': 'pehd', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 2.520265649788176e-05, 'nb_photo': 2, 'list_port_cont': '6678458', 'assoc_port': '6678032:6678458', 'assoc_mat': '6678032:pehd:4209:6678458'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0009691335779369147, 'nb_photo': 50, 'list_port_cont': '6862208,6862361,6862535,6862580,6862650,6867917,6868229,6868248,6868281,6868376,6868672,6869102,6869324,6869466,6869550,6877011,6877046,6877191,6877677,6877777,6877812,6877923,6877993,6878035,6886875,6888290,6891353,6896153,6898498,6904509,6907044,6917257,6922719,6925591', 'assoc_port': '6790887:6862535,6832752:6877923,6833565:6878035,6834821:6877993,6836050:6877777,6838973:6877677,6842390:6877812,6846972:6869550,6846973:6869466,6848417:6869324,6851003:6868672,6852533:6869102,6861512:6868229,6861514:6868281,6861516:6868248,6861517:6868376,6861519:6862580,6861520:6862650,6861524:6862208,6861635:6862361,6864848:6867917,6876683:6877191,6876685:6877011,6876686:6877046,6886258:6886875,6887695:6888290,6889190:6891353,6895749:6896153,6898175:6898498,6904057:6904509,6906036:6907044,6917033:6917257,6921897:6922719,6925484:6925591', 'assoc_mat': '6790887:pet_clair:4207:6862535,6832752:pet_clair:4207:6877923,6833565:pet_clair:4207:6878035,6834821:pet_clair:4207:6877993,6836050:pet_clair:4207:6877777,6838973:pet_clair:4207:6877677,6842390:pet_clair:4207:6877812,6846972:pet_clair:4207:6869550,6846973:pet_clair:4207:6869466,6848417:pet_clair:4207:6869324,6851003:pet_clair:4207:6868672,6852533:pet_clair:4207:6869102,6861512:pet_clair:4207:6868229,6861514:pet_clair:4207:6868281,6861516:pet_clair:4207:6868248,6861517:pet_clair:4207:6868376,6861519:pet_clair:4207:6862580,6861520:pet_clair:4207:6862650,6861524:pet_clair:4207:6862208,6861635:pet_clair:4207:6862361,6864848:pet_clair:4207:6867917,6876683:pet_clair:4207:6877191,6876685:pet_clair:4207:6877011,6876686:pet_clair:4207:6877046,6886258:pet_clair:4207:6886875,6887695:pet_clair:4207:6888290,6889190:pet_clair:4207:6891353,6895749:pet_clair:4207:6896153,6898175:pet_clair:4207:6898498,6904057:pet_clair:4207:6904509,6906036:pet_clair:4207:6907044,6917033:pet_clair:4207:6917257,6921897:pet_clair:4207:6922719,6'}, {'unwanted_material': 'pet_clair', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.00038788845724592604, 'nb_photo': 52, 'list_port_cont': '6613103,6648162,6655833,6657176,6658378,6660889,6666083,6666651,6666947,6668915,6669550,6669705,6669777,6671157,6700228,6704711,6706879,6709548,6712610,6720621,6722887,6723439,6744920,6748621,6748942,6761453,6774102,6776478,6837016,6841130,6846026,6853934', 'assoc_port': '6612572:6613103,6630822:6666083,6631385:6668915,6631618:6669777,6632901:6671157,6635653:6660889,6647202:6648162,6655088:6655833,6656505:6657176,6656506:6658378,6665067:6666947,6665718:6666651,6668654:6669705,6668657:6669550,6699726:6700228,6700555:6704711,6706262:6706879,6707499:6709548,6708643:6712610,6720202:6720621,6722573:6722887,6723094:6723439,6744086:6744920,6747584:6748621,6748313:6748942,6761126:6761453,6773386:6774102,6776077:6776478,6836050:6837016,6840554:6841130,6845438:6846026,6852534:6853934', 'assoc_mat': '6612572:pet_clair:4209:6613103,6630822:pet_clair:4209:6666083,6631385:pet_clair:4209:6668915,6631618:pet_clair:4209:6669777,6632901:pet_clair:4209:6671157,6635653:pet_clair:4209:6660889,6647202:pet_clair:4209:6648162,6655088:pet_clair:4209:6655833,6656505:pet_clair:4209:6657176,6656506:pet_clair:4209:6658378,6665067:pet_clair:4209:6666947,6665718:pet_clair:4209:6666651,6668654:pet_clair:4209:6669705,6668657:pet_clair:4209:6669550,6699726:pet_clair:4209:6700228,6700555:pet_clair:4209:6704711,6706262:pet_clair:4209:6706879,6707499:pet_clair:4209:6709548,6708643:pet_clair:4209:6712610,6720202:pet_clair:4209:6720621,6722573:pet_clair:4209:6722887,6723094:pet_clair:4209:6723439,6744086:pet_clair:4209:6744920,6747584:pet_clair:4209:6748621,6748313:pet_clair:4209:6748942,6761126:pet_clair:4209:6761453,6773386:pet_clair:4209:6774102,6776077:pet_clair:4209:6776478,6836050:pet_clair:4209:6837016,6840554:pet_clair:4209:6841130,6845438:pet_clair:4209:6846026,6852534:pet_clair:4209:6853934'}, {'unwanted_material': 'pet_clair', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.00042088720046359597, 'nb_photo': 1, 'list_port_cont': '6862605', 'assoc_port': '6861515:6862605', 'assoc_mat': '6861515:pet_clair:4209:6862605'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0008578698895049885, 'nb_photo': 28, 'list_port_cont': '6860798,6861839,6862207,6868256,6868671,6869549,6877674,6877708,6877816,6877879,6878008,6886869,6888301,6889467,6898496,6904520,6915175,6922716', 'assoc_port': '6832753:6877879,6834821:6878008,6838973:6877674,6840554:6877708,6842390:6877816,6846972:6869549,6851003:6868671,6855642:6860798,6859123:6861839,6861516:6868256,6861524:6862207,6886258:6886869,6887695:6888301,6887698:6889467,6898175:6898496,6904057:6904520,6914259:6915175,6921897:6922716', 'assoc_mat': '6832753:pet_fonce:4207:6877879,6834821:pet_fonce:4207:6878008,6838973:pet_fonce:4207:6877674,6840554:pet_fonce:4207:6877708,6842390:pet_fonce:4207:6877816,6846972:pet_fonce:4207:6869549,6851003:pet_fonce:4207:6868671,6855642:pet_fonce:4207:6860798,6859123:pet_fonce:4207:6861839,6861516:pet_fonce:4207:6868256,6861524:pet_fonce:4207:6862207,6886258:pet_fonce:4207:6886869,6887695:pet_fonce:4207:6888301,6887698:pet_fonce:4207:6889467,6898175:pet_fonce:4207:6898496,6904057:pet_fonce:4207:6904520,6914259:pet_fonce:4207:6915175,6921897:pet_fonce:4207:6922716'}, {'unwanted_material': 'pet_fonce', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.002001176734086916, 'nb_photo': 165, 'list_port_cont': '6613106,6614596,6626457,6628830,6629210,6635370,6641607,6642548,6644426,6646347,6647448,6648893,6654377,6659140,6659225,6660883,6661079,6664546,6664700,6665227,6666079,6666644,6666756,6666950,6668662,6669252,6669485,6669785,6671575,6674270,6678461,6683425,6683978,6702227,6704714,6706892,6708972,6709423,6709577,6711293,6712608,6718881,6719276,6719647,6720634,6722892,6723506,6744910,6748597,6762003,6774109,6776983,6841135,6842934,6847565,6859391', 'assoc_port': '6612572:6613106,6614336:6614596,6625827:6626457,6628455:6628830,6628736:6629210,6630577:6664546,6630578:6664700,6630581:6665227,6630822:6666079,6631384:6668662,6631618:6669785,6633545:6635370,6635651:6661079,6635653:6660883,6639123:6644426,6639695:6642548,6640928:6641607,6645791:6646347,6646682:6647448,6648217:6648893,6652800:6654377,6657819:6659140,6659025:6659225,6665067:6666950,6665718:6666644,6665719:6666756,6668655:6669252,6668656:6669485,6670886:6671575,6673850:6674270,6678032:6678461,6682633:6683425,6683289:6683978,6700555:6704714,6701501:6702227,6706262:6706892,6706760:6709423,6707163:6708972,6707499:6709577,6708643:6712608,6708644:6711293,6718520:6719276,6718521:6718881,6719441:6719647,6720202:6720634,6722573:6722892,6723192:6723506,6744086:6744910,6747584:6748597,6759349:6762003,6773386:6774109,6775574:6776983,6840554:6841135,6842390:6842934,6846972:6847565,6859123:6859391', 'assoc_mat': '6612572:pet_fonce:4209:6613106,6614336:pet_fonce:4209:6614596,6625827:pet_fonce:4209:6626457,6628455:pet_fonce:4209:6628830,6628736:pet_fonce:4209:6629210,6630577:pet_fonce:4209:6664546,6630578:pet_fonce:4209:6664700,6630581:pet_fonce:4209:6665227,6630822:pet_fonce:4209:6666079,6631384:pet_fonce:4209:6668662,6631618:pet_fonce:4209:6669785,6633545:pet_fonce:4209:6635370,6635651:pet_fonce:4209:6661079,6635653:pet_fonce:4209:6660883,6639123:pet_fonce:4209:6644426,6639695:pet_fonce:4209:6642548,6640928:pet_fonce:4209:6641607,6645791:pet_fonce:4209:6646347,6646682:pet_fonce:4209:6647448,6648217:pet_fonce:4209:6648893,6652800:pet_fonce:4209:6654377,6657819:pet_fonce:4209:6659140,6659025:pet_fonce:4209:6659225,6665067:pet_fonce:4209:6666950,6665718:pet_fonce:4209:6666644,6665719:pet_fonce:4209:6666756,6668655:pet_fonce:4209:6669252,6668656:pet_fonce:4209:6669485,6670886:pet_fonce:4209:6671575,6673850:pet_fonce:4209:6674270,6678032:pet_fonce:4209:6678461,6682633:pet_fonce:4209:6683425,6683289:pet_fonce:4209:6683978,6'}, {'unwanted_material': 'pet_fonce', 'main_material': 'gm', 'pht_type': 4209, 'ratio': 0.0009882492230258019, 'nb_photo': 2, 'list_port_cont': '6894805', 'assoc_port': '6894658:6894805', 'assoc_mat': '6894658:pet_fonce:4209:6894805'}, {'unwanted_material': 'pet_fonce', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.9832720072705387, 'nb_photo': 21, 'list_port_cont': '6647700,6861697', 'assoc_port': '6647200:6647700,6861521:6861697', 'assoc_mat': '6647200:pet_fonce:4200:6647700,6861521:pet_fonce:4200:6861697'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4207, 'ratio': 0.0006428090611349251, 'nb_photo': 9, 'list_port_cont': '6866496,6868369,6869096,6869546,6878036,6904487,6905142,6905991,6922715', 'assoc_port': '6833565:6878036,6846972:6869546,6852533:6869096,6861517:6868369,6864855:6866496,6903710:6905991,6903712:6904487,6905102:6905142,6921897:6922715', 'assoc_mat': '6833565:pet_opaque:4207:6878036,6846972:pet_opaque:4207:6869546,6852533:pet_opaque:4207:6869096,6861517:pet_opaque:4207:6868369,6864855:pet_opaque:4207:6866496,6903710:pet_opaque:4207:6905991,6903712:pet_opaque:4207:6904487,6905102:pet_opaque:4207:6905142,6921897:pet_opaque:4207:6922715'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005744915736568625, 'nb_photo': 214, 'list_port_cont': '6613105,6614118,6616031,6627858,6628408,6628839,6629216,6629282,6635308,6635367,6639706,6640660,6641595,6644422,6647441,6655823,6657172,6658373,6659149,6660897,6661077,6664543,6664634,6664804,6664940,6665229,6666078,6666656,6666757,6666952,6667994,6668664,6668914,6669254,6669547,6669695,6669784,6669814,6670110,6671566,6671848,6672292,6674152,6676774,6678456,6681180,6683428,6683979,6700223,6704719,6706884,6709575,6711305,6712607,6718352,6718715,6719280,6719890,6720396,6722151,6722589,6722891,6723435,6744915,6748617,6748952,6749035,6760254,6761440,6774110,6776978,6813183,6834135,6834365,6834702,6835809,6839582,6841141,6842938,6853225,6853523,6859402', 'assoc_port': '6612572:6613105,6613636:6614118,6614891:6616031,6627447:6627858,6627801:6628408,6627802:6629282,6628455:6628839,6628736:6629216,6630577:6664543,6630579:6664634,6630580:6664940,6630581:6665229,6630822:6666078,6631154:6667994,6631384:6668664,6631385:6668914,6631618:6669784,6631619:6669814,6631901:6670110,6633543:6635308,6633544:6671848,6633545:6635367,6633546:6672292,6635651:6661077,6635653:6660897,6639123:6644422,6639124:6639706,6640066:6640660,6640928:6641595,6646682:6647441,6655088:6655823,6656505:6657172,6656506:6658373,6657819:6659149,6663042:6664804,6665067:6666952,6665718:6666656,6665719:6666757,6668654:6669695,6668655:6669254,6668657:6669547,6670886:6671566,6671756:6676774,6673385:6674152,6678032:6678456,6680947:6681180,6682633:6683428,6683289:6683979,6699726:6700223,6700555:6704719,6706262:6706884,6707499:6709575,6708643:6712607,6708644:6711305,6715973:6718352,6716500:6718715,6717201:6719890,6718520:6719280,6719951:6720396,6721826:6722589,6721827:6722151,6722573:6722891,6723094:6723435,6744086:6744915,', 'assoc_mat': '6612572:pet_opaque:4209:6613105,6613636:pet_opaque:4209:6614118,6614891:pet_opaque:4209:6616031,6627447:pet_opaque:4209:6627858,6627801:pet_opaque:4209:6628408,6627802:pet_opaque:4209:6629282,6628455:pet_opaque:4209:6628839,6628736:pet_opaque:4209:6629216,6630577:pet_opaque:4209:6664543,6630579:pet_opaque:4209:6664634,6630580:pet_opaque:4209:6664940,6630581:pet_opaque:4209:6665229,6630822:pet_opaque:4209:6666078,6631154:pet_opaque:4209:6667994,6631384:pet_opaque:4209:6668664,6631385:pet_opaque:4209:6668914,6631618:pet_opaque:4209:6669784,6631619:pet_opaque:4209:6669814,6631901:pet_opaque:4209:6670110,6633543:pet_opaque:4209:6635308,6633544:pet_opaque:4209:6671848,6633545:pet_opaque:4209:6635367,6633546:pet_opaque:4209:6672292,6635651:pet_opaque:4209:6661077,6635653:pet_opaque:4209:6660897,6639123:pet_opaque:4209:6644422,6639124:pet_opaque:4209:6639706,6640066:pet_opaque:4209:6640660,6640928:pet_opaque:4209:6641595,6646682:pet_opaque:4209:6647441,6655088:pet_opaque:4209:6655823,6656505:pet_opaque:4209:6657172,'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(448643,449568,454450,454465,454459,454472,456239,458795,461498,454567,457375,457649,459844,460382,461634,463066,472528,465865,469297,468129,475039,472453,474892,474538,475316,476524,479832,481665,479903)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! None was removed should we ? donnée sous forme de texte was removed should we ? [ptf_id0,ptf_id1...] was removed should we ? {'emr': {'barquette_opaque': ['barquette_opaque', '0.01%', 44], 'carton': ['carton', '0.78%', 1462], 'ela': ['ela', '0.01%', 3], 'etiquette': ['etiquette', '0.08%', 1049], 'film_plastique': ['film_plastique', '0.01%', 9], 'kraft': ['kraft', '0.11%', 1], 'metal': ['metal', '2.6%', 787], 'papier': ['papier', '96.86%', 148871], 'pehd': ['pehd', '0.0%', 2], 'pet_clair': ['pet_clair', '0.04%', 52], 'pet_fonce': ['pet_fonce', '0.2%', 165], 'pet_opaque': ['pet_opaque', '0.06%', 214]}, 'aluminium': {'carton': ['carton', '1.02%', 16], 'metal': ['metal', '98.97%', 54], 'papier': ['papier', '0.13%', 9]}, 'gm': {'carton': ['carton', '0.29%', 10], 'etiquette': ['etiquette', '0.08%', 16], 'metal': ['metal', '3.43%', 14], 'papier': ['papier', '99.03%', 2955], 'pet_clair': ['pet_clair', '0.04%', 1], 'pet_fonce': ['pet_fonce', '0.1%', 2]}, 'pet_fonce': {'carton': ['carton', '0.59%', 4], 'etiquette': ['etiquette', '0.11%', 5], 'papier': ['papier', '1.02%', 31], 'pet_fonce': ['pet_fonce', '98.33%', 21]}} filepath : /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf voici le hostname : marlene hash: 9b473a9b7e6f69a2b2147971a7bf4221 for path: /home/admin/workarea/git/Velours/python/prod/memo/sla_mensuel/sla_mensuel_Romainville_Presse_2_mois_08_annee_2022.pdf ############################### TEST one_day ################################ TODO and TOTEST Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_one_day nb_day : (3, 30) nb deleted : 3 VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier Inconsistency in dashboard_places dashboard_places from input : romainville_petite_presse dashboard_name_from_port found from datou STS from crontab from raspi : Romainville_Presse_1 We force the correct camera_place_name ! TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO TODO : Insert select and so on ***** analysis of device with port 20001 for dashboard romainville_petite_presse ***** **** analysis of day 2022/09/01/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-01 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/02/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-02 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis of day 2022/09/03/ **** VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! WARNING: No hour configured for port 20001, from 00:00 to 24:00 used 0:00:00 apple pause between two photos 10 Unable to retrieve photo time from log, find in sqlite. Find filename in sqlite. Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 5716, not duplicated 5865, two criteria ok 5716, nb forced upload 0 end of day status of photos as found in sqllite Unable to find info for dashboard number 181 for day 2022-09-03 00:00:00 Info from dashboard total number of uploaded images : 0 number of useful images : 5716 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) **** analysis for all days **** Info from logs total number of images : 0 coverage for 10 seconds : 0:00:00 (0.00%) coverage for 20 seconds : 0:00:00 (0.00%) max time between two photos : 0:00:00 results of pre-diagnostic : light ok 17148, not duplicated 17595, two criteria ok 17148, nb forced upload 0 end of day status of photos as found in sqllite Info from dashboard total number of uploaded images : 0 number of useful images : 17148 number of finished photos : 0 (0.00% of two criteria ok) number of finished photos for older algorithm : 0 (0.00% of two criteria ok) number of started photos : 0 (0.00% of two criteria ok) cvs resume : date,nb_photos,% time,nb ok,uploaded,to upload, % uploaded, nb useful, % completed photos, last_update, remark 2022/09/01,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/02,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, 2022/09/03,0,0.00%,5716,0,0,0.00%,0,0.00%,0000/00/00, coverage for this period for every 10 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 coverage for this period for every 20 second 2022/09/01/ 0.0 2022/09/02/ 0.0 2022/09/03/ 0.0 mean value for this period : 0.0 ############################### TEST get_data ################################ TODO and TOTEST VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier nb_day : (0, 31) VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select id from MTRUser.mtr_portfolios where id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(select last_run_id from MTRPhoto.dashboard_entry_day where dashboard_place_id in (select id from MTRPhoto.dashboard_places where name = 'Romainville_Presse_1' and date like '%2022-08%') and created_at like '%2022-08%'))); nb_day : (0, 31) after unwanted_material_data nb_day : (0, 31) after coverage_data after number_of_batch date_start : 2022-08-01 : dt_date_just_month_year : 2022-08-01 00:00:00 : VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! after pl.get_datou_sts_from_crontab : verbose : False no sts found, try to find from database SELECT dri.id FROM MTRPhoto.dashboard_run_ids dri, MTRPhoto.dashboard_entry_day ded, MTRPhoto.dashboard_places dp WHERE dp.name= "Romainville_Presse_1" AND ded.dashboard_place_id=dp.id AND dri.dashboard_entry_day=ded.id AND dri.id=ded.last_run_id AND ded.date >= "2022-08-01" AND ded.date <= "2022-08-31" apple3 {'gm': {'mat': 'gm', 'pht': 4209, 'datou_carac_id': 3994, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'emr': {'mat': 'emr', 'pht': 4207, 'datou_carac_id': 3993, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'carton,background'}, 'jrm': {'mat': 'jrm', 'pht': 3726, 'datou_carac_id': 3459, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'papier,background'}, 'ela': {'mat': 'ela', 'pht': 4203, 'datou_carac_id': 3991, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'ela,background'}, 'pet_clair': {'mat': 'pet_clair', 'pht': 3327, 'datou_carac_id': 3804, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_clair,bouchon,etiquette,barquette_avec_film,background'}, 'film_pedb': {'mat': 'film_pedb', 'pht': 4461, 'datou_carac_id': 4322, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'film_plastique'}, 'pehd_pp': {'mat': 'pehd_pp', 'pht': 4211, 'datou_carac_id': 3995, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pehd,background'}, 'pet_fonce': {'mat': 'pet_fonce', 'pht': 4200, 'datou_carac_id': 4153, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'pet_fonce,background'}, 'aluminium': {'mat': 'aluminium', 'pht': 4205, 'datou_carac_id': 3992, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'metal,background'}, 'refus': {'mat': 'refus', 'pht': 3594, 'datou_carac_id': 3318, 'unwanted_material': [], 'hashtag_majoritaire_from_carac': 'refus,background'}} SELECT h.hashtag as unwanted_material, substr(dr.hashtag,8) as main_material, ptp.type as pht_type, sum(pcr.value*dr.nombre_balle)/sum(dr.nombre_balle) as ratio, count(distinct mpp.mtr_photo_id) as nb_photo, group_concat(distinct ptp.mtr_portfolio_id_2) as list_port_cont, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_port, group_concat(distinct concat(cast(ptp.mtr_portfolio_id_1 as char), ":", h.hashtag, ":", cast(ptp.type as char), ":", cast(ptp.mtr_portfolio_id_2 as char))) as assoc_mat FROM MTRPhoto.dashboard_results dr, MTRPhoto.mtr_port_to_port_ids ptp, MTRUser.mtr_portfolio_photos mpp, MTRUser.portfolio_carac_ratio pcr, MTRBack.hashtags h WHERE dr.dashboard_run_id IN (449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886) AND dr.mtr_portfolio_id=ptp.mtr_portfolio_id_1 AND dr.qualite >= 0 AND mpp.mtr_portfolio_id=ptp.mtr_portfolio_id_2 AND pcr.portfolio_id=ptp.mtr_portfolio_id_1 AND h.hashtag_id = pcr.hashtag_id AND ptp.type = pcr.hashtag_type AND mpp.hide_status = 0 AND ptp.hashtag_id=h.hashtag_id AND ptp.type IN (4209,4207,3726,4203,3327,4461,4211,4200,4205,3594) group by h.hashtag, dr.hashtag, ptp.type; VR TODO TO BETTER PARSE ! ({'unwanted_material': 'autre', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.025985557831013643, 'nb_photo': 16651, 'list_port_cont': '6601211,6601249,6601319,6601376,6601558,6601691,6601783,6601837,6602312,6603611,6603837,6603984,6604939,6606283,6606361,6607127,6607396,6607889,6608318,6608592,6608684,6608960,6609911,6610060,6610278,6610907,6611078,6613725,6614148,6615899,6616064,6616199,6616975,6617461,6617846,6618177,6618947,6620505,6620992,6621119,6621146,6621174,6621388,6621447,6621537,6622105,6622162,6622311,6622378,6622426,6622514,6622647,6622758,6626372,6626974,6626997,6627200,6627837,6628024,6628557,6628624,6629354,6629396,6635806,6635864,6635897,6635947,6635974,6636029,6636109,6636139,6636295,6636316,6636423,6636599,6636674,6636727,6636791,6637048,6637689,6637708,6637756,6637854,6637954,6637974,6638095,6638421,6638597,6638696,6638833,6638947,6638968,6639187,6639299,6639353,6639652,6639761,6639844,6639904,6639965,6640123,6640725,6640745,6640776,6640959,6641209,6641306,6641333,6641484,6641521,6641655,6641820,6641954,6642026,6642079,6642285,6643237,6643372,6643479,6643603,6643631,6643664,6643695,6643718,6643895,6644078,6644458,6644663,', 'assoc_port': '6600535:6601249,6600537:6601691,6600543:6601211,6600545:6601376,6600547:6601558,6600550:6601783,6600553:6601319,6601140:6602312,6601199:6601837,6602727:6603837,6602729:6603984,6602732:6603611,6604400:6604939,6604702:6606361,6605500:6606283,6605502:6607127,6606682:6608318,6606685:6607889,6606687:6607396,6607836:6608592,6607838:6608684,6608144:6608960,6609197:6610060,6609198:6609911,6609963:6610278,6610497:6611078,6610499:6610907,6612953:6613725,6613333:6614148,6614966:6616199,6614968:6616064,6615360:6615899,6616171:6616975,6616960:6618177,6616966:6617461,6616967:6617846,6618310:6618947,6620036:6620505,6620039:6621388,6620042:6620992,6620404:6621146,6620441:6621447,6620445:6621174,6620446:6621537,6620449:6621119,6621639:6622647,6621642:6622758,6621645:6622514,6621650:6622378,6621652:6622426,6621655:6622162,6621656:6622105,6621661:6622311,6625901:6626372,6626270:6626974,6626272:6626997,6626275:6627200,6627097:6627837,6627099:6628024,6627946:6628557,6627948:6628624,6628988:6629354,6628991:6629396,6630847:6635947,', 'assoc_mat': '6600535:autre:3327:6601249,6600537:autre:3327:6601691,6600543:autre:3327:6601211,6600545:autre:3327:6601376,6600547:autre:3327:6601558,6600550:autre:3327:6601783,6600553:autre:3327:6601319,6601140:autre:3327:6602312,6601199:autre:3327:6601837,6602727:autre:3327:6603837,6602729:autre:3327:6603984,6602732:autre:3327:6603611,6604400:autre:3327:6604939,6604702:autre:3327:6606361,6605500:autre:3327:6606283,6605502:autre:3327:6607127,6606682:autre:3327:6608318,6606685:autre:3327:6607889,6606687:autre:3327:6607396,6607836:autre:3327:6608592,6607838:autre:3327:6608684,6608144:autre:3327:6608960,6609197:autre:3327:6610060,6609198:autre:3327:6609911,6609963:autre:3327:6610278,6610497:autre:3327:6611078,6610499:autre:3327:6610907,6612953:autre:3327:6613725,6613333:autre:3327:6614148,6614966:autre:3327:6616199,6614968:autre:3327:6616064,6615360:autre:3327:6615899,6616171:autre:3327:6616975,6616960:autre:3327:6618177,6616966:autre:3327:6617461,6616967:autre:3327:6617846,6618310:autre:3327:6618947,6620036:autre:3327:662050'}, {'unwanted_material': 'barquette_avec_film', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.0050634087913797535, 'nb_photo': 1051, 'list_port_cont': '6601216,6601255,6601316,6601366,6601556,6601694,6601795,6601849,6602314,6603610,6603845,6603981,6604938,6606272,6606380,6607136,6607388,6607885,6608324,6608593,6608675,6608967,6609910,6610048,6610273,6611082,6613721,6614147,6615898,6616193,6616972,6617468,6618176,6618950,6620509,6620988,6621144,6621167,6621391,6621449,6622102,6622163,6622316,6622379,6622436,6622519,6622752,6626364,6626973,6627004,6627197,6627840,6628023,6628564,6628623,6629352,6629394,6635811,6635862,6635901,6635945,6635972,6636036,6636112,6636138,6636318,6636424,6636724,6637044,6637685,6637716,6637764,6637852,6637945,6637984,6638094,6638596,6638700,6638835,6638944,6638974,6639298,6639361,6639645,6639764,6639852,6639896,6639968,6640121,6640722,6640783,6640963,6641211,6641294,6641332,6641486,6641520,6641652,6641944,6642022,6642080,6642290,6643373,6643478,6643608,6643683,6643716,6643902,6644079,6644456,6644652,6645162,6645304,6645766,6647408,6648376,6648805,6649738,6651383,6651686,6654487,6654608,6655045,6655877,6656213,6656668,6659085,6660221,', 'assoc_port': '6600535:6601255,6600537:6601694,6600543:6601216,6600545:6601366,6600547:6601556,6600550:6601795,6600553:6601316,6601140:6602314,6601199:6601849,6602727:6603845,6602729:6603981,6602732:6603610,6604400:6604938,6604702:6606380,6605500:6606272,6605502:6607136,6606682:6608324,6606685:6607885,6606687:6607388,6607836:6608593,6607838:6608675,6608144:6608967,6609197:6610048,6609198:6609910,6609963:6610273,6610497:6611082,6612953:6613721,6613333:6614147,6614966:6616193,6615360:6615898,6616171:6616972,6616960:6618176,6616966:6617468,6618310:6618950,6620036:6620509,6620039:6621391,6620042:6620988,6620404:6621144,6620441:6621449,6620445:6621167,6621642:6622752,6621645:6622519,6621650:6622379,6621652:6622436,6621655:6622163,6621656:6622102,6621661:6622316,6625901:6626364,6626270:6626973,6626272:6627004,6626275:6627197,6627097:6627840,6627099:6628023,6627946:6628564,6627948:6628623,6628988:6629352,6628991:6629394,6630847:6635945,6630849:6636036,6630851:6635972,6630854:6637685,6630934:6637764,6630936:6637716,6630938:6638094,', 'assoc_mat': '6600535:barquette_avec_film:3327:6601255,6600537:barquette_avec_film:3327:6601694,6600543:barquette_avec_film:3327:6601216,6600545:barquette_avec_film:3327:6601366,6600547:barquette_avec_film:3327:6601556,6600550:barquette_avec_film:3327:6601795,6600553:barquette_avec_film:3327:6601316,6601140:barquette_avec_film:3327:6602314,6601199:barquette_avec_film:3327:6601849,6602727:barquette_avec_film:3327:6603845,6602729:barquette_avec_film:3327:6603981,6602732:barquette_avec_film:3327:6603610,6604400:barquette_avec_film:3327:6604938,6604702:barquette_avec_film:3327:6606380,6605500:barquette_avec_film:3327:6606272,6605502:barquette_avec_film:3327:6607136,6606682:barquette_avec_film:3327:6608324,6606685:barquette_avec_film:3327:6607885,6606687:barquette_avec_film:3327:6607388,6607836:barquette_avec_film:3327:6608593,6607838:barquette_avec_film:3327:6608675,6608144:barquette_avec_film:3327:6608967,6609197:barquette_avec_film:3327:6610048,6609198:barquette_avec_film:3327:6609910,6609963:barquette_avec_film:3327:6610273'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0006065490165070008, 'nb_photo': 9, 'list_port_cont': '6603426,6627334,6634749,6723417,6780640,6834099,6852168', 'assoc_port': '6602731:6603426,6626721:6627334,6630937:6634749,6671808:6834099,6722813:6723417,6780026:6780640,6851650:6852168', 'assoc_mat': '6602731:barquette_opaque:4203:6603426,6626721:barquette_opaque:4203:6627334,6630937:barquette_opaque:4203:6634749,6671808:barquette_opaque:4203:6834099,6722813:barquette_opaque:4203:6723417,6780026:barquette_opaque:4203:6780640,6851650:barquette_opaque:4203:6852168'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.0005293780694215035, 'nb_photo': 1, 'list_port_cont': '6692906', 'assoc_port': '6691778:6692906', 'assoc_mat': '6691778:barquette_opaque:4209:6692906'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.008662761320534096, 'nb_photo': 659, 'list_port_cont': '6600860,6600903,6601023,6601433,6602273,6602593,6603403,6606082,6607689,6608069,6608289,6609462,6610653,6614532,6615427,6615792,6616525,6618779,6618922,6620328,6621053,6621664,6621960,6622002,6622135,6622199,6624057,6626607,6627268,6628481,6629183,6629252,6634382,6635139,6638289,6638441,6640989,6642058,6642706,6650155,6650276,6650959,6658495,6664041,6668175,6669610,6672035,6674014,6674549,6674630,6674772,6675081,6675105,6675139,6675482,6676524,6677132,6677217,6678042,6678216,6678577,6678660,6678688,6678769,6678960,6678988,6681252,6687138,6692292,6693194,6693681,6693891,6694045,6694217,6694420,6698421,6698805,6699377,6699427,6700669,6707231,6711232,6711409,6711495,6712142,6713437,6713607,6713753,6714397,6716562,6717498,6717555,6718025,6719023,6719736,6720660,6720723,6721143,6721925,6722677,6723045,6723102,6724740,6724912,6724973,6725830,6729866,6730263,6730718,6731855,6732324,6732440,6735375,6738136,6740197,6740602,6741090,6741939,6743256,6743597,6743876,6745278,6747827,6751581,6751678,6751683,6752336,6754140,', 'assoc_port': '10844008:10846065,10844009:10845784,10844011:10845552,10844012:10845981,10844014:10845176,10844016:10845161,10844017:10845129,10844018:10844946,10844019:10845048,10844020:10844679,12107427:12108055,12107429:12107821,6600536:6601433,6600544:6600860,6600546:6601023,6600551:6600903,6601138:6602593,6601649:6602273,6602733:6603403,6604399:6608069,6605499:6610653,6605688:6606082,6606683:6607689,6607839:6608289,6609199:6609462,6614199:6614532,6614969:6615427,6615362:6615792,6616173:6616525,6618307:6618779,6618311:6618922,6620040:6620328,6620422:6621664,6620444:6621053,6621638:6624057,6621647:6622199,6621651:6622135,6621654:6621960,6621659:6622002,6626268:6626607,6627098:6627268,6627945:6628481,6628987:6629183,6628989:6629252,6630852:6674014,6630932:6674630,6630935:6674772,6630939:6675105,6631065:6675081,6631069:6675482,6631193:6635139,6631477:6676524,6631489:6677132,6631492:6677217,6631967:6634382,6632585:6678216,6632660:6678577,6632663:6678660,6632714:6678769,6632787:6678688,6632877:6678960,6632881:6678988,6635669:', 'assoc_mat': '10844008:barquette_opaque:4211:10846065,10844009:barquette_opaque:4211:10845784,10844011:barquette_opaque:4211:10845552,10844012:barquette_opaque:4211:10845981,10844014:barquette_opaque:4211:10845176,10844016:barquette_opaque:4211:10845161,10844017:barquette_opaque:4211:10845129,10844018:barquette_opaque:4211:10844946,10844019:barquette_opaque:4211:10845048,10844020:barquette_opaque:4211:10844679,12107427:barquette_opaque:4211:12108055,12107429:barquette_opaque:4211:12107821,6600536:barquette_opaque:4211:6601433,6600544:barquette_opaque:4211:6600860,6600546:barquette_opaque:4211:6601023,6600551:barquette_opaque:4211:6600903,6601138:barquette_opaque:4211:6602593,6601649:barquette_opaque:4211:6602273,6602733:barquette_opaque:4211:6603403,6604399:barquette_opaque:4211:6608069,6605499:barquette_opaque:4211:6610653,6605688:barquette_opaque:4211:6606082,6606683:barquette_opaque:4211:6607689,6607839:barquette_opaque:4211:6608289,6609199:barquette_opaque:4211:6609462,6614199:barquette_opaque:4211:6614532,6614969:barq'}, {'unwanted_material': 'barquette_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.0009915902324084817, 'nb_photo': 5, 'list_port_cont': '6649286,6687459,6784020,6813915,6920756', 'assoc_port': '6631070:6649286,6686295:6687459,6782249:6784020,6813389:6813915,6920100:6920756', 'assoc_mat': '6631070:barquette_opaque:4200:6649286,6686295:barquette_opaque:4200:6687459,6782249:barquette_opaque:4200:6784020,6813389:barquette_opaque:4200:6813915,6920100:barquette_opaque:4200:6920756'}, {'unwanted_material': 'bouchon', 'main_material': 'pet_clair', 'pht_type': 3327, 'ratio': 0.002148587132747826, 'nb_photo': 2481, 'list_port_cont': 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'assoc_port': 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{'unwanted_material': 'pet_opaque', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 0.0009264502462378221, 'nb_photo': 9, 'list_port_cont': '6608814,6660097,6712722,6735145,6799275,6808109,6869931', 'assoc_port': '6608146:6608814,6632879:6660097,6685476:6799275,6690887:6735145,6712332:6712722,6786466:6808109,6869544:6869931', 'assoc_mat': '6608146:pet_opaque:4203:6608814,6632879:pet_opaque:4203:6660097,6685476:pet_opaque:4203:6799275,6690887:pet_opaque:4203:6735145,6712332:pet_opaque:4203:6712722,6786466:pet_opaque:4203:6808109,6869544:pet_opaque:4203:6869931'}, {'unwanted_material': 'pet_opaque', 'main_material': 'emr', 'pht_type': 4209, 'ratio': 0.007255625347838711, 'nb_photo': 19, 'list_port_cont': '6616740,6617521,6620419,6621938,6627043,6638500,6668139,6668209,6670564,6670896,6678093,6740485,6791207', 'assoc_port': 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'6600901,6602283,6603013,6606069,6608294,6610401,6610657,6611146,6614528,6615400,6621062,6621675,6621998,6622129,6622205,6626613,6629184,6629244,6634651,6635837,6638286,6639164,6650157,6668179,6669616,6672038,6674023,6674634,6674773,6675099,6675136,6677119,6677811,6678215,6678578,6678957,6679224,6681250,6691510,6691659,6693900,6694213,6694417,6698434,6698768,6698799,6699430,6711411,6711493,6712146,6712497,6712754,6713439,6716561,6717501,6717546,6718028,6719452,6720657,6720722,6724914,6725690,6725833,6728841,6729870,6730329,6730727,6731863,6732437,6735376,6738126,6740205,6740607,6741938,6743382,6745286,6751579,6751673,6754142,6754990,6755968,6756060,6756975,6762171,6763524,6767274,6768410,6773018,6774679,6775056,6778932,6780264,6780486,6783061,6791499,6793831,6794248,6794400,6795572,6796823,6799103,6799441,6799779,6800492,6800530,6801272,6801402,6806174,6808133,6808682,6808713,6808832,6808873,6809086,6810005,6815419,6822330,6839899,6842610,6853846,6855228,6855448,6863478,6866679,6869888,6870654,6875922,6890225,', 'assoc_port': '10844011:10845551,10844021:10844440,6600551:6600901,6601649:6602283,6602728:6603013,6605499:6610657,6605688:6606069,6607835:6610401,6607839:6608294,6610992:6611146,6614199:6614528,6614967:6615400,6620422:6621675,6620444:6621062,6621647:6622205,6621651:6622129,6621659:6621998,6626268:6626613,6628987:6629184,6628989:6629244,6630852:6674023,6630856:6634651,6630932:6674634,6630935:6674773,6630939:6675099,6631489:6677119,6631960:6677811,6632585:6678215,6632660:6678578,6632877:6678957,6632963:6679224,6633862:6635837,6635671:6693900,6635682:6694213,6637236:6698768,6637845:6638286,6638776:6639164,6640595:6806174,6647215:6717546,6648944:6650157,6653523:6725690,6654038:6724914,6654837:6725833,6655426:6728841,6656895:6729870,6657891:6731863,6666361:6668179,6668952:6751579,6668953:6669616,6671805:6756060,6671810:6672038,6672701:6755968,6674723:6675136,6681005:6681250,6683620:6691510,6685431:6799103,6685494:6799441,6685525:6799779,6686006:6855228,6686021:6855448,6686094:6800530,6686269:6800492,6686302:6801272,6686308:6801', 'assoc_mat': '10844011:pet_opaque:4211:10845551,10844021:pet_opaque:4211:10844440,6600551:pet_opaque:4211:6600901,6601649:pet_opaque:4211:6602283,6602728:pet_opaque:4211:6603013,6605499:pet_opaque:4211:6610657,6605688:pet_opaque:4211:6606069,6607835:pet_opaque:4211:6610401,6607839:pet_opaque:4211:6608294,6610992:pet_opaque:4211:6611146,6614199:pet_opaque:4211:6614528,6614967:pet_opaque:4211:6615400,6620422:pet_opaque:4211:6621675,6620444:pet_opaque:4211:6621062,6621647:pet_opaque:4211:6622205,6621651:pet_opaque:4211:6622129,6621659:pet_opaque:4211:6621998,6626268:pet_opaque:4211:6626613,6628987:pet_opaque:4211:6629184,6628989:pet_opaque:4211:6629244,6630852:pet_opaque:4211:6674023,6630856:pet_opaque:4211:6634651,6630932:pet_opaque:4211:6674634,6630935:pet_opaque:4211:6674773,6630939:pet_opaque:4211:6675099,6631489:pet_opaque:4211:6677119,6631960:pet_opaque:4211:6677811,6632585:pet_opaque:4211:6678215,6632660:pet_opaque:4211:6678578,6632877:pet_opaque:4211:6678957,6632963:pet_opaque:4211:6679224,6633862:pet_opaque:4211:6635'}, {'unwanted_material': 'pet_opaque', 'main_material': 'pet_fonce', 'pht_type': 4200, 'ratio': 0.006625372408584668, 'nb_photo': 447, 'list_port_cont': '6600927,6602060,6604867,6606114,6609165,6613073,6615618,6618507,6620186,6620865,6621897,6626785,6628362,6634254,6635389,6637069,6637443,6638357,6639233,6641563,6646016,6647330,6649289,6649475,6649908,6653440,6654682,6655700,6658472,6662384,6665186,6669577,6670052,6672606,6674518,6683671,6684478,6686802,6687225,6687464,6696442,6696817,6698458,6711562,6712277,6712786,6714508,6717607,6719819,6720158,6720206,6720247,6720358,6721243,6722532,6724146,6739282,6740800,6745521,6745789,6750245,6754191,6754564,6759415,6765265,6766702,6767358,6780406,6780528,6780714,6784016,6786843,6788645,6791330,6793704,6795548,6797668,6817665,6821371,6824096,6835190,6840200,6842443,6849228,6851781,6852374,6854530,6859022,6862735,6865768,6866326,6867982,6869259,6874403,6875763,6882730,6886804,6888096,6889498,6895283,6900338,6902503,6904834,6914452,6920753,6921628,6924247', 'assoc_port': '6600538:6600927,6601136:6602060,6604397:6604867,6605689:6606114,6608145:6609165,6612632:6613073,6615365:6615618,6618309:6618507,6620043:6620186,6620439:6620865,6621653:6621897,6626271:6626785,6627944:6628362,6630848:6646016,6631070:6649289,6631186:6634254,6631194:6649908,6631961:6655700,6632583:6662384,6632786:6635389,6632966:6670052,6635673:6637443,6635681:6637069,6637843:6638357,6638774:6639233,6641286:6641563,6646447:6647330,6648940:6649475,6650951:6653440,6654041:6654682,6657889:6658472,6664429:6665186,6668950:6669577,6671809:6672606,6673895:6674518,6681680:6683671,6683618:6684478,6685440:6720358,6685511:6720206,6686027:6719819,6686041:6720158,6686295:6687464,6686312:6687225,6686325:6686802,6688871:6714508,6690894:6698458,6693641:6696442,6696476:6696817,6710860:6711562,6711680:6712277,6712324:6712786,6716688:6717607,6718843:6720247,6720803:6721243,6722354:6722532,6723892:6724146,6738748:6739282,6740018:6740800,6740709:6745521,6744268:6745789,6748062:6750245,6753406:6754564,6753419:6754191,6759057:6759415,', 'assoc_mat': '6600538:pet_opaque:4200:6600927,6601136:pet_opaque:4200:6602060,6604397:pet_opaque:4200:6604867,6605689:pet_opaque:4200:6606114,6608145:pet_opaque:4200:6609165,6612632:pet_opaque:4200:6613073,6615365:pet_opaque:4200:6615618,6618309:pet_opaque:4200:6618507,6620043:pet_opaque:4200:6620186,6620439:pet_opaque:4200:6620865,6621653:pet_opaque:4200:6621897,6626271:pet_opaque:4200:6626785,6627944:pet_opaque:4200:6628362,6630848:pet_opaque:4200:6646016,6631070:pet_opaque:4200:6649289,6631186:pet_opaque:4200:6634254,6631194:pet_opaque:4200:6649908,6631961:pet_opaque:4200:6655700,6632583:pet_opaque:4200:6662384,6632786:pet_opaque:4200:6635389,6632966:pet_opaque:4200:6670052,6635673:pet_opaque:4200:6637443,6635681:pet_opaque:4200:6637069,6637843:pet_opaque:4200:6638357,6638774:pet_opaque:4200:6639233,6641286:pet_opaque:4200:6641563,6646447:pet_opaque:4200:6647330,6648940:pet_opaque:4200:6649475,6650951:pet_opaque:4200:6653440,6654041:pet_opaque:4200:6654682,6657889:pet_opaque:4200:6658472,6664429:pet_opaque:4200:6665186,'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'ela', 'pht_type': 4203, 'ratio': 5.317677982697793e-05, 'nb_photo': 1, 'list_port_cont': '6754069', 'assoc_port': '6753416:6754069', 'assoc_mat': '6753416:textiles_sanitaires:4203:6754069'}, {'unwanted_material': 'textiles_sanitaires', 'main_material': 'pehd_pp', 'pht_type': 4211, 'ratio': 0.00014015295179752374, 'nb_photo': 14, 'list_port_cont': '6608063,6675111,6678967,6709734,6720656,6724749,6725824,6735649,6787860,6791503,6813248,6828520,6855445,10845973', 'assoc_port': '10844012:10845973,6604399:6608063,6630939:6675111,6632877:6678967,6654837:6725824,6663625:6735649,6686021:6855445,6709374:6709734,6720298:6720656,6724591:6724749,6787210:6787860,6790919:6791503,6812962:6813248,6814154:6828520', 'assoc_mat': '10844012:textiles_sanitaires:4211:10845973,6604399:textiles_sanitaires:4211:6608063,6630939:textiles_sanitaires:4211:6675111,6632877:textiles_sanitaires:4211:6678967,6654837:textiles_sanitaires:4211:6725824,6663625:textiles_sanitaires:4211:6735649,6686021:textiles_sanitaires:4211:6855445,6709374:textiles_sanitaires:4211:6709734,6720298:textiles_sanitaires:4211:6720656,6724591:textiles_sanitaires:4211:6724749,6787210:textiles_sanitaires:4211:6787860,6790919:textiles_sanitaires:4211:6791503,6812962:textiles_sanitaires:4211:6813248,6814154:textiles_sanitaires:4211:6828520'}) select count(distinct mtr_photo_id) from MTRUser.mtr_portfolio_photos where mtr_portfolio_id in (select mtr_portfolio_id from MTRPhoto.dashboard_results where dashboard_run_id in(449271,454376,454388,454411,454399,454424,451519,452689,468274,455185,456478,457640,459828,461074,462392,463490,464885,465870,469411,470781,897152,474755,474338,475306,477478,478927,479886)); after get_hostname_from_raspi hasthag : emr hasthag that could be used but not yet : _______carton,background hasthag : jrm hasthag that could be used but not yet : _______papier,background hasthag : aluminium hasthag that could be used but not yet : _______metal,background hasthag : pet_fonce hasthag that could be used but not yet : _______pet_fonce,background hasthag : gm hasthag that could be used but not yet : _______papier,background after impurety_average_per_hashtag ############################### TEST csv ################################ Removing /home/admin/workarea/git/Velours/python/prod/memo/sla_csv VR TODO TOCHECK : due to this bug it shouldn't have being able to work, or maybe it was due to a change directory not done ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier TODO : Insert select and so on # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) DONE and to test : checkNoCycle ! We are managing only one step so we do not consider checkConsistencyNbInputNbOutput ! We are managing only one step so we do not consider checkConsistencyTypeOutputInput ! # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11488 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11496 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11497 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11492 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11492 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11495 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11495 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11489 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11575 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11575 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11491 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11490 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11490 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11498 send_mail_cod have less outputs used (0) than in the step definition (1) : some outputs may be not used ! Step 11499 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11491 doesn't seem to be define in the database( WARNING : type of input 3 of step 11490 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11488 doesn't seem to be define in the database( WARNING : type of input 2 of step 11492 doesn't seem to be define in the database( WARNING : output 1 of step 11488 have datatype=2 whereas input 1 of step 11495 have datatype=7 WARNING : type of output 2 of step 11495 doesn't seem to be define in the database( WARNING : type of input 1 of step 11489 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11491 have datatype=10 whereas input 3 of step 11498 have datatype=6 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11575 doesn't seem to be define in the database( WARNING : output 1 of step 11489 have datatype=7 whereas input 2 of step 11575 have datatype=None WARNING : type of output 3 of step 11575 doesn't seem to be define in the database( WARNING : type of input 1 of step 11491 doesn't seem to be define in the database( WARNING : output 0 of step 11491 have datatype=10 whereas input 0 of step 11581 have datatype=18 WARNING : type of input 5 of step 11498 doesn't seem to be define in the database( WARNING : output 0 of step 11581 have datatype=11 whereas input 5 of step 11498 have datatype=None WARNING : type of output 1 of step 11496 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : type of output 1 of step 11497 doesn't seem to be define in the database( WARNING : type of input 3 of step 11492 doesn't seem to be define in the database( WARNING : output 0 of step 11495 have datatype=1 whereas input 0 of step 11489 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4209, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'papier,background', 'hashtag_background': 'papier', 'hashtag_weights': {'barquette_opaque': 0.7, 'carton': 0.7, 'ela': 0.7, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.7, 'metal': 1.5, 'pehd': 0.7, 'pet_clair': 0.7, 'pet_opaque': 0.7, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.7}, 'ETA': 600} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11500 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11508 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11509 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11504 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11504 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11507 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11507 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11501 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11576 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11576 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11503 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11502 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11502 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11511 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11503 doesn't seem to be define in the database( WARNING : type of input 3 of step 11502 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11500 doesn't seem to be define in the database( WARNING : type of input 2 of step 11504 doesn't seem to be define in the database( WARNING : output 1 of step 11500 have datatype=2 whereas input 1 of step 11507 have datatype=7 WARNING : type of output 2 of step 11507 doesn't seem to be define in the database( WARNING : type of input 1 of step 11501 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11503 have datatype=10 whereas input 3 of step 11510 have datatype=6 WARNING : type of input 2 of step 11576 doesn't seem to be define in the database( WARNING : output 1 of step 11501 have datatype=7 whereas input 2 of step 11576 have datatype=None WARNING : type of output 3 of step 11576 doesn't seem to be define in the database( WARNING : type of input 1 of step 11503 doesn't seem to be define in the database( WARNING : output 0 of step 11503 have datatype=10 whereas input 0 of step 11582 have datatype=18 WARNING : type of input 5 of step 11510 doesn't seem to be define in the database( WARNING : output 0 of step 11582 have datatype=11 whereas input 5 of step 11510 have datatype=None WARNING : type of output 1 of step 11508 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : type of output 1 of step 11509 doesn't seem to be define in the database( WARNING : type of input 3 of step 11504 doesn't seem to be define in the database( WARNING : output 0 of step 11507 have datatype=1 whereas input 0 of step 11501 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4207, 'hashtag_proportion': 'barquette_opaque,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'carton,background', 'hashtag_weights': {'barquette_opaque': 1, 'ela': 1, 'etiquette': 1.0, 'film_plastique': 0.5, 'kraft': 1, 'metal': 3.0, 'papier': 1, 'pehd': 2, 'pet_clair': 2, 'pet_opaque': 2, 'textiles_sanitaires': 1.0, 'pet_fonce': 2}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11449 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11452 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Step 11452 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11453 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11453 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11454 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11478 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11478 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11456 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11455 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11455 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11458 send_mail_cod have less inputs used (3) than in the step definition (5) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11449 doesn't seem to be define in the database( WARNING : type of input 2 of step 11452 doesn't seem to be define in the database( WARNING : output 1 of step 11449 have datatype=2 whereas input 1 of step 11453 have datatype=7 WARNING : type of output 2 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11454 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 3 of step 11453 doesn't seem to be define in the database( WARNING : type of input 1 of step 11456 doesn't seem to be define in the database( WARNING : type of output 1 of step 11456 doesn't seem to be define in the database( WARNING : type of input 3 of step 11455 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11456 have datatype=10 whereas input 3 of step 11458 have datatype=6 WARNING : type of input 5 of step 11458 doesn't seem to be define in the database( WARNING : output 0 of step 11477 have datatype=11 whereas input 5 of step 11458 have datatype=None WARNING : output 0 of step 11456 have datatype=10 whereas input 0 of step 11477 have datatype=18 WARNING : type of input 2 of step 11478 doesn't seem to be define in the database( WARNING : output 1 of step 11454 have datatype=7 whereas input 2 of step 11478 have datatype=None WARNING : type of output 3 of step 11478 doesn't seem to be define in the database( WARNING : type of input 2 of step 11456 doesn't seem to be define in the database( WARNING : output 0 of step 11453 have datatype=1 whereas input 0 of step 11454 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3726, 'hashtag_proportion': 'Carton_brun,Carton_gris,Teint_Dans_La_Masse,autre_refus,cartonnette,kraft,metal,plastique', 'hashtag_parmi': 'papier,background', 'hashtag_weights': {'Carton_brun': 1.5, 'Carton_gris': 1.5, 'Teint_Dans_La_Masse': 1.0, 'autre_refus': 1.5, 'cartonnette': 1.0, 'kraft': 1.5, 'metal': 3, 'plastique': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11512 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11521 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11520 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11516 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11516 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11519 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11519 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11513 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11577 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11577 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11515 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11514 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11514 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11523 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11515 doesn't seem to be define in the database( WARNING : type of input 3 of step 11514 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11512 doesn't seem to be define in the database( WARNING : type of input 2 of step 11516 doesn't seem to be define in the database( WARNING : output 1 of step 11512 have datatype=2 whereas input 1 of step 11519 have datatype=7 WARNING : type of output 2 of step 11519 doesn't seem to be define in the database( WARNING : type of input 1 of step 11513 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11515 have datatype=10 whereas input 3 of step 11522 have datatype=6 WARNING : type of input 2 of step 11577 doesn't seem to be define in the database( WARNING : output 1 of step 11513 have datatype=7 whereas input 2 of step 11577 have datatype=None WARNING : type of output 3 of step 11577 doesn't seem to be define in the database( WARNING : type of input 1 of step 11515 doesn't seem to be define in the database( WARNING : output 0 of step 11515 have datatype=10 whereas input 0 of step 11583 have datatype=18 WARNING : type of input 5 of step 11522 doesn't seem to be define in the database( WARNING : output 0 of step 11583 have datatype=11 whereas input 5 of step 11522 have datatype=None WARNING : type of output 1 of step 11521 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : type of output 1 of step 11520 doesn't seem to be define in the database( WARNING : type of input 3 of step 11516 doesn't seem to be define in the database( WARNING : output 0 of step 11519 have datatype=1 whereas input 0 of step 11513 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4203, 'hashtag_proportion': 'barquette_opaque,carton,etiquette,film_plastique,kraft,metal,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'ela,background', 'hashtag_background': 'ela', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1, 'etiquette': 0.5, 'film_plastique': 0.5, 'kraft': 0.8, 'metal': 2, 'papier': 0.8, 'pehd': 0.8, 'pet_clair': 0.8, 'pet_opaque': 0.8, 'textiles_sanitaires': 0.5, 'pet_fonce': 0.8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11560 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 11567 mask_detect have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11567 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11563 crop_condition is not consistent : 4 used against 2 in the step definition ! Step 11563 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11564 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11564 merge_mask_thcl_custom is not consistent : 4 used against 2 in the step definition ! WARNING : number of inputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11565 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11573 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11573 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11568 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11566 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11566 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 1 of step 11560 have datatype=2 whereas input 1 of step 11564 have datatype=7 WARNING : type of output 2 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11565 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11567 doesn't seem to be define in the database( WARNING : type of input 3 of step 11563 doesn't seem to be define in the database( WARNING : type of output 3 of step 11564 doesn't seem to be define in the database( WARNING : type of input 1 of step 11568 doesn't seem to be define in the database( WARNING : type of output 1 of step 11568 doesn't seem to be define in the database( WARNING : type of input 3 of step 11566 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11570 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of input 2 of step 11563 doesn't seem to be define in the database( WARNING : output 0 of step 11569 have datatype=6 whereas input 2 of step 11563 have datatype=None WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11570 doesn't seem to be define in the database( WARNING : type of output 2 of step 11560 doesn't seem to be define in the database( WARNING : type of input 1 of step 11569 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11568 have datatype=10 whereas input 3 of step 11571 have datatype=6 WARNING : type of input 2 of step 11573 doesn't seem to be define in the database( WARNING : output 1 of step 11565 have datatype=7 whereas input 2 of step 11573 have datatype=None WARNING : type of output 3 of step 11573 doesn't seem to be define in the database( WARNING : type of input 3 of step 11568 doesn't seem to be define in the database( WARNING : output 0 of step 11568 have datatype=10 whereas input 0 of step 11587 have datatype=18 WARNING : type of input 5 of step 11571 doesn't seem to be define in the database( WARNING : output 0 of step 11587 have datatype=11 whereas input 5 of step 11571 have datatype=None WARNING : output 0 of step 11564 have datatype=1 whereas input 0 of step 11565 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3327, 'hashtag_proportion': 'autre,carton,metal,papier,pehd,pet_fonce', 'hashtag_parmi': 'pet_clair,bouchon,etiquette,barquette_avec_film,background', 'hashtag_background': 'pet_clair', 'hashtag_weights': {'autre': 8.0, 'barquette_avec_film': 6, 'carton': 8.0, 'metal': 12, 'papier': 5, 'pehd': 8, 'pet_fonce': 8, 'bouchon': 8, 'etiquette': 8}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11978 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11987 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11986 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11982 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11982 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11985 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11985 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11979 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11990 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11990 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11981 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11980 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11980 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11989 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11981 doesn't seem to be define in the database( WARNING : type of input 3 of step 11980 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11978 doesn't seem to be define in the database( WARNING : type of input 2 of step 11982 doesn't seem to be define in the database( WARNING : output 1 of step 11978 have datatype=2 whereas input 1 of step 11985 have datatype=7 WARNING : type of output 2 of step 11985 doesn't seem to be define in the database( WARNING : type of input 1 of step 11979 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11981 have datatype=10 whereas input 3 of step 11988 have datatype=6 WARNING : type of input 2 of step 11990 doesn't seem to be define in the database( WARNING : output 1 of step 11979 have datatype=7 whereas input 2 of step 11990 have datatype=None WARNING : type of output 3 of step 11990 doesn't seem to be define in the database( WARNING : type of input 1 of step 11981 doesn't seem to be define in the database( WARNING : output 0 of step 11981 have datatype=10 whereas input 0 of step 11991 have datatype=18 WARNING : type of input 5 of step 11988 doesn't seem to be define in the database( WARNING : output 0 of step 11991 have datatype=11 whereas input 5 of step 11988 have datatype=None WARNING : type of output 1 of step 11987 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : type of output 1 of step 11986 doesn't seem to be define in the database( WARNING : type of input 3 of step 11982 doesn't seem to be define in the database( WARNING : output 0 of step 11985 have datatype=1 whereas input 0 of step 11979 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4461, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,pehd,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'film_plastique', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11524 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11533 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11532 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11528 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11528 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11531 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11531 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11525 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11578 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11578 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11527 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11526 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11526 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11535 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11527 doesn't seem to be define in the database( WARNING : type of input 3 of step 11526 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11524 doesn't seem to be define in the database( WARNING : type of input 2 of step 11528 doesn't seem to be define in the database( WARNING : output 1 of step 11524 have datatype=2 whereas input 1 of step 11531 have datatype=7 WARNING : type of output 2 of step 11531 doesn't seem to be define in the database( WARNING : type of input 1 of step 11525 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11527 have datatype=10 whereas input 3 of step 11534 have datatype=6 WARNING : type of input 2 of step 11578 doesn't seem to be define in the database( WARNING : output 1 of step 11525 have datatype=7 whereas input 2 of step 11578 have datatype=None WARNING : type of output 3 of step 11578 doesn't seem to be define in the database( WARNING : type of input 1 of step 11527 doesn't seem to be define in the database( WARNING : output 0 of step 11527 have datatype=10 whereas input 0 of step 11584 have datatype=18 WARNING : type of input 5 of step 11534 doesn't seem to be define in the database( WARNING : output 0 of step 11584 have datatype=11 whereas input 5 of step 11534 have datatype=None WARNING : type of output 1 of step 11533 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : type of output 1 of step 11532 doesn't seem to be define in the database( WARNING : type of input 3 of step 11528 doesn't seem to be define in the database( WARNING : output 0 of step 11531 have datatype=1 whereas input 0 of step 11525 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4211, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,metal,papier,pet_fonce,pet_clair,pet_opaque,textiles_sanitaires', 'hashtag_parmi': 'pehd,background', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 0.3, 'ela': 0.3, 'etiquette': 0.1, 'film_plastique': 0.1, 'kraft': 0.3, 'metal': 1.5, 'papier': 0.3, 'pet_clair': 0.3, 'pet_opaque': 0.3, 'textiles_sanitaires': 0.3, 'pet_fonce': 0.3, 'background': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11548 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11556 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11557 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11552 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11552 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11555 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11555 merge_mask_thcl_custom is not consistent : 3 used against 2 in the step definition ! WARNING : number of inputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11549 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11580 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11580 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11551 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11550 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11550 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11559 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11551 doesn't seem to be define in the database( WARNING : type of input 3 of step 11550 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11548 doesn't seem to be define in the database( WARNING : type of input 2 of step 11552 doesn't seem to be define in the database( WARNING : output 1 of step 11548 have datatype=2 whereas input 1 of step 11555 have datatype=7 WARNING : type of output 2 of step 11555 doesn't seem to be define in the database( WARNING : type of input 1 of step 11549 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11551 have datatype=10 whereas input 3 of step 11558 have datatype=6 WARNING : type of input 2 of step 11580 doesn't seem to be define in the database( WARNING : output 1 of step 11549 have datatype=7 whereas input 2 of step 11580 have datatype=None WARNING : type of output 3 of step 11580 doesn't seem to be define in the database( WARNING : type of input 1 of step 11551 doesn't seem to be define in the database( WARNING : output 0 of step 11551 have datatype=10 whereas input 0 of step 11586 have datatype=18 WARNING : type of input 5 of step 11558 doesn't seem to be define in the database( WARNING : output 0 of step 11586 have datatype=11 whereas input 5 of step 11558 have datatype=None WARNING : type of output 1 of step 11556 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : type of output 1 of step 11557 doesn't seem to be define in the database( WARNING : type of input 3 of step 11552 doesn't seem to be define in the database( WARNING : output 0 of step 11555 have datatype=1 whereas input 0 of step 11549 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4200, 'hashtag_proportion': 'carton,ela,etiquette,film_plastique,kraft,metal,papier,pehd,pet_opaque,textiles_sanitaires,barquette_opaque,pet_clair', 'hashtag_parmi': 'pet_fonce,background', 'hashtag_weights': {'barquette_opaque': 1.5, 'carton': 2.5, 'ela': 1.5, 'etiquette': 1.5, 'film_plastique': 1, 'kraft': 1.5, 'metal': 3.0, 'papier': 1.2, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 11536 mask_detect is not consistent : 3 used against 2 in the step definition ! WARNING : number of outputs for step 11545 blur_detection is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 11544 brightness is not consistent : 2 used against 1 in the step definition ! WARNING : number of inputs for step 11540 crop_condition is not consistent : 3 used against 2 in the step definition ! Step 11540 crop_condition have less outputs used (2) than in the step definition (3) : some outputs may be not used ! Step 11543 merge_mask_thcl_custom have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11543 merge_mask_thcl_custom have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 11537 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! Step 11579 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 11579 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of outputs for step 11539 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 11538 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 11538 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! Step 11547 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 11539 doesn't seem to be define in the database( WARNING : type of input 3 of step 11538 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 2 of step 11536 doesn't seem to be define in the database( WARNING : type of input 2 of step 11540 doesn't seem to be define in the database( WARNING : output 1 of step 11536 have datatype=2 whereas input 1 of step 11543 have datatype=7 We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 11539 have datatype=10 whereas input 3 of step 11546 have datatype=6 WARNING : type of input 2 of step 11579 doesn't seem to be define in the database( WARNING : output 1 of step 11537 have datatype=7 whereas input 2 of step 11579 have datatype=None WARNING : type of output 3 of step 11579 doesn't seem to be define in the database( WARNING : type of input 1 of step 11539 doesn't seem to be define in the database( WARNING : output 0 of step 11539 have datatype=10 whereas input 0 of step 11585 have datatype=18 WARNING : type of input 5 of step 11546 doesn't seem to be define in the database( WARNING : output 0 of step 11585 have datatype=11 whereas input 5 of step 11546 have datatype=None WARNING : type of output 1 of step 11545 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : type of output 1 of step 11544 doesn't seem to be define in the database( WARNING : type of input 3 of step 11540 doesn't seem to be define in the database( WARNING : output 0 of step 11543 have datatype=1 whereas input 0 of step 11537 have datatype=2 DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 4205, 'hashtag_proportion': 'barquette_opaque,carton,ela,etiquette,film_plastique,kraft,papier,pehd,pet_clair,pet_opaque,textiles_sanitaires,pet_fonce', 'hashtag_parmi': 'metal,background', 'hashtag_background': 'metal', 'hashtag_weights': {'barquette_opaque': 1, 'carton': 1.5, 'ela': 1.5, 'etiquette': 1, 'film_plastique': 1, 'kraft': 1, 'papier': 1, 'pehd': 1.5, 'pet_clair': 1.5, 'pet_opaque': 1.5, 'textiles_sanitaires': 1.5, 'pet_fonce': 1.5}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier # VR 17-11-17 : to create in DB ! Here we check the datou graph and we reorder steps ! Tree builded and cycle checked, now we need to re-order the steps ! We have currenlty an error because there is no dependence between the last step for the case tile - detect - glue We can either keep the depence of, it is better to keep an order compatible with the id of steps if we do not have sons, so a lexical order : (number_son, step_id) All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! All sons are already in current list ! DONE and to test : checkNoCycle ! Here we check the consistency of inputs/outputs number between the given ones and the db ! eke 1-6-18 : checkConsistencyNbInputNbOutput should be processed after step reordering ! WARNING : number of outputs for step 7928 mask_detect is not consistent : 3 used against 2 in the step definition ! Step 8092 crop_condition have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! WARNING : number of outputs for step 8092 crop_condition is not consistent : 4 used against 3 in the step definition ! WARNING : number of inputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7933 rle_unique_nms_with_priority is not consistent : 2 used against 1 in the step definition ! WARNING : number of outputs for step 7935 ventilate_hashtags_in_portfolio is not consistent : 2 used against 1 in the step definition ! Step 7934 final have less inputs used (2) than in the step definition (3) : maybe we manage optionnal inputs ! Step 7934 final have less outputs used (1) than in the step definition (2) : some outputs may be not used ! WARNING : number of outputs for step 13649 velours_tree is not consistent : 2 used against 1 in the step definition ! Step 9283 split_time_score have less inputs used (1) than in the step definition (2) : maybe we manage optionnal inputs ! Number of inputs / outputs for each step checked ! Here we check the consistency of outputs/inputs types during steps connections eke 1-6-18 : checkConsistencyTypeOutputInput should be processed after checkConsistencyNbInputNbOutput ! We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of output 1 of step 7935 doesn't seem to be define in the database( WARNING : type of input 3 of step 7934 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : type of input 1 of step 7935 doesn't seem to be define in the database( WARNING : output 1 of step 7933 have datatype=7 whereas input 1 of step 7935 have datatype=None WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 2 of step 8092 doesn't seem to be define in the database( WARNING : type of output 3 of step 8092 doesn't seem to be define in the database( WARNING : type of input 1 of step 7933 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10917 doesn't seem to be define in the database( WARNING : type of output 2 of step 7928 doesn't seem to be define in the database( WARNING : type of input 1 of step 10918 doesn't seem to be define in the database( We ignore checkConsistencyTypeOutputInput for datou_step final ! WARNING : output 0 of step 7935 have datatype=10 whereas input 3 of step 10916 have datatype=6 WARNING : output 0 of step 7935 have datatype=10 whereas input 0 of step 13649 have datatype=18 WARNING : type of output 1 of step 13649 doesn't seem to be define in the database( WARNING : type of input 5 of step 10916 doesn't seem to be define in the database( DataTypes for each output/input checked ! Unexpected error treated as WARNING for now expected given param in final step {'name_pipeline': 'aggregate_carac_ratio', 'hashtag_type': 3594, 'hashtag_proportion': 'papier,carton,metal,pet_clair,autre,pehd,pet_fonce', 'hashtag_parmi': 'refus,background', 'hashtag_weights': {'papier': 1, 'carton': 1, 'metal': 1, 'pet_clair': 1, 'autre': 1, 'pehd': 1, 'pet_fonce': 1, 'refus': 1}} Found hashtag_parmi in final step, will be used and useful if different from matiere_majoritaire in classifier create_report_from_API() got an unexpected keyword argument 'outfolder' ERROR csv FAILED #&_#_#&_# TEST memo SUCCEEDED #&_#_#&_# #&_#_#&_# TEST one_day SUCCEEDED #&_#_#&_# #&_#_#&_# TEST get_data SUCCEEDED #&_#_#&_# #&_#_#&_# TEST csv FAILED #&_#_#&_# #&_# TEST FAILED #&_# : prod/memo/memo #&_# #&_# END OF TEST #&_# : prod/memo/memo #&_# /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/../../tools/../lib/rpn/proposal_layer.py:28: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. layer_params = yaml.load(self.param_str_) /usr/lib/python3/dist-packages/paramiko/transport.py:220: CryptographyDeprecationWarning: Blowfish has been deprecated "class": algorithms.Blowfish, #######all_test_python_finish###### insert result in bdd : Test are not running on charlot, the path should be tested ! /data_2/data_log/job/2025/April/23042025/python_test3/output_tests_python-1131.html new path : /proc/1620143/ /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None Name Stmts Miss Cover Missing ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/SHA256.py 46 29 37% 72-80, 89-93, 104-112, 122, 135-140, 145, 158, 171-185 /home/admin/.local/lib/python3.8/site-packages/Crypto/Hash/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Numbers.py 11 7 36% 36-42 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/Primality.py 154 141 8% 65-116, 134-213, 244-277, 314-335, 354-369 /home/admin/.local/lib/python3.8/site-packages/Crypto/Math/_IntegerBase.py 226 121 46% 43, 47, 51, 55, 60, 65, 69, 73, 77, 81, 85, 89, 94, 99, 103, 107, 111, 115, 119, 123, 127, 131, 135, 139, 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426-475, 518-618, 635-638, 646-647, 652-655, 660-661, 665-670, 676-688, 693-712, 756-791 /home/admin/.local/lib/python3.8/site-packages/Crypto/PublicKey/__init__.py 33 29 12% 46-60, 65-73, 79-94 /home/admin/.local/lib/python3.8/site-packages/Crypto/Random/__init__.py 16 6 62% 31, 35, 39, 43, 48, 52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/PKCS1_v1_5.py 14 9 36% 42-46, 49-52 /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Signature/pkcs1_15.py 49 39 20% 49, 53, 73-84, 106-138, 191-207, 221 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_file_system.py 8 1 88% 46 /home/admin/.local/lib/python3.8/site-packages/Crypto/Util/_raw_api.py 164 105 36% 42-46, 61, 66, 77, 99, 113, 118-126, 130, 134, 137-143, 149, 152, 155, 158, 162-261, 268-269, 272, 275-276, 279-284, 304-305, 307-309, 314, 318 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158, 169, 175-176, 182-183, 189, 199, 209-210, 214, 218-220, 224-228, 232-239, 243-254, 259-273, 287-290, 293, 296-297, 301-306, 309-320, 323-324, 340-348, 357-358, 361, 387-397, 400-401, 405-413, 421-422, 426, 434-435, 439, 447-448, 452, 455-456, 478-490, 494, 500-523, 528-556, 560, 563-582, 618-619, 628-636, 639-641, 650-656, 659, 663-664, 667, 670, 678-687, 695-703, 706, 710-712, 718-732, 737-739, 746-761, 799-804, 811-813, 817-831, 835-836, 876-878, 885-890, 894-898, 914, 933, 936-943, 951-954, 957-969, 979-1002, 1009-1013, 1021, 1024-1041, 1065-1068, 1072-1090, 1097-1118, 1121-1157, 1169-1174, 1180-1191, 1206-1212, 1215-1227, 1231-1233, 1257-1261, 1268-1283, 1286-1288, 1301-1306, 1312-1329, 1384-1401, 1404-1408, 1411, 1421-1458, 1466-1512, 1515-1522, 1528, 1535-1566, 1572-1573, 1576-1584, 1587-1614, 1617-1624, 1629-1639, 1660-1668, 1671, 1674-1678, 1681-1686, 1689-1692, 1695-1725, 1733, 1746, 1757-1760, 1772-1798, 1806-1810, 1814-1816, 1827-1832, 1837-1838, 1841-1848, 1858-1861, 1866-1882, 1885, 1895, 1911-1915, 1931-1932, 1941-1947, 1959, 1962-1965, 1973-1985, 1994-2000, 2009-2021, 2025-2027, 2031-2057, 2075-2083, 2086-2111, 2114-2128 /home/admin/.local/lib/python3.8/site-packages/botocore/discovery.py 183 139 24% 40-42, 47, 51-52, 56-60, 63-67, 70-75, 78, 83-90, 95-107, 110-115, 118-121, 124-125, 128, 131-133, 138-142, 145-148, 151-160, 163-168, 171, 174-214, 219, 222-228, 231-236, 239-251, 254-274 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/__init__.py 11 8 27% 28-38 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/docstringparser.py 119 83 30% 24-26, 29-30, 34-36, 39-42, 45, 48, 51, 61-64, 67-79, 82-87, 90, 93, 98, 101, 106-107, 110-111, 114, 117-118, 126-128, 131-133, 136-138, 141-143, 148, 151-152, 160-170, 178-181, 184, 187-200 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/restdoc.py 112 74 34% 25-33, 36-37, 43, 49, 56, 62, 68, 74-78, 81, 84-85, 88-97, 101-103, 120-128, 133, 141, 145, 149, 153, 156-157, 175-183, 187, 191, 201-209, 212, 215, 218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/bcdoc/style.py 288 206 28% 22-25, 29, 33, 36, 39, 42-43, 46, 49, 52, 55, 58, 61, 64, 70-73, 76, 79, 82, 91-101, 104, 107, 110-111, 114-115, 118-121, 124-126, 129-132, 135, 138, 141, 144, 147, 150-153, 156-157, 160-161, 164-165, 168-169, 172-175, 178-181, 184-185, 188-191, 194-195, 198-201, 204-205, 208-219, 222, 225-230, 233-254, 257-258, 261-262, 265-267, 270-271, 274-277, 280-283, 286-289, 293-296, 299-302, 305, 308, 311, 314, 317-319, 322-323, 332-334, 337-342, 345-351, 354-357, 360-361, 364-368, 371-374, 377-378, 381-387, 390-391, 394-397, 400-401, 404-406, 409-412, 415-418 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/client.py 170 130 24% 28-32, 39-43, 46, 49-67, 71, 75-81, 84-86, 90-94, 97, 100, 103-110, 113-132, 167-168, 171-174, 177, 180-192, 195-196, 199-210, 213-214, 217-223, 226-229, 232-246, 249-254, 257-264, 267-276, 281-290 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/docstring.py 38 19 50% 33-36, 40, 43, 58-60, 63, 70-72, 75-81, 86, 91, 96 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/example.py 127 107 16% 36-41, 46, 50-56, 60-66, 70-81, 85-110, 114-125, 128-132, 135-139, 142-146, 152-158, 166-169, 177-208 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/method.py 97 86 11% 33-39, 60-78, 99-105, 117-123, 169-281 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/paginator.py 49 39 20% 22-24, 31-43, 46-65, 94-167 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/params.py 132 108 18% 33-34, 39, 43, 47-56, 60-77, 82-95, 98, 101-105, 108-117, 120-121, 124-125, 134-145, 149, 159-177, 182-212, 215-216, 219-220 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/service.py 53 36 32% 24-32, 46-55, 58-59, 66, 69-75, 78, 81-88, 91-96, 99-102 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/shape.py 39 31 21% 27-32, 61-89, 92-98, 101-107, 110-117 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/sharedexample.py 143 123 14% 33-38, 41-55, 58-72, 88-97, 101-125, 128-147, 150-164, 169-170, 173, 176-180, 183-187, 190-192, 195-198, 213-218 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/utils.py 75 33 56% 27, 50, 69-77, 117-128, 160-166, 176-180, 197 /home/admin/.local/lib/python3.8/site-packages/botocore/docs/waiter.py 35 25 29% 22-24, 31-38, 41-56, 83-119 /home/admin/.local/lib/python3.8/site-packages/botocore/endpoint.py 153 117 24% 54-70, 85-94, 97, 100-102, 105-118, 122-124, 127-128, 131-158, 166-183, 186-230, 236-245, 249-266, 269, 274, 285-303, 314, 323-327 /home/admin/.local/lib/python3.8/site-packages/botocore/errorfactory.py 32 21 34% 28, 44, 47-51, 58, 70-74, 77-88 /home/admin/.local/lib/python3.8/site-packages/botocore/eventstream.py 246 149 39% 33-34, 40-43, 49-52, 58-61, 71-72, 110, 123, 135-136, 148-149, 161-162, 175-176, 189-190, 203-204, 225-229, 251-253, 265, 279, 285-287, 293-295, 306, 318, 328, 334-337, 340-343, 384, 396-397, 400-406, 409-411, 414-416, 419-421, 424-428, 431, 442-444, 452, 455-459, 462-468, 471-472, 475-477, 480-483, 487-488, 491-494, 497-502, 506-507, 515-524, 527, 530, 577-581, 584-587, 590-594, 597-602, 605-612, 616 /home/admin/.local/lib/python3.8/site-packages/botocore/exceptions.py 200 32 84% 24-28, 40-42, 45, 82-84, 87, 369-370, 410-420, 423-430, 436, 620-622 /home/admin/.local/lib/python3.8/site-packages/botocore/handlers.py 434 313 28% 80, 90-106, 125-134, 138-152, 163-185, 189-198, 202-205, 210-214, 218-224, 228-236, 246, 256, 260-272, 276, 285, 289-297, 305-312, 316-330, 365-374, 378-392, 396-401, 414-420, 427-429, 434-443, 450-465, 475-477, 483-496, 505-514, 518-522, 544-562, 573-584, 588-593, 597-598, 603-605, 620-637, 658-668, 672, 676-690, 696-699, 703-707, 719, 732, 745, 763-773, 777-781, 822-835, 838-855, 858-862, 875, 885, 894-900, 903-905, 911-923, 927-929, 934-935 /home/admin/.local/lib/python3.8/site-packages/botocore/history.py 27 10 63% 22, 31, 34, 37, 40-47 /home/admin/.local/lib/python3.8/site-packages/botocore/hooks.py 249 196 21% 31-35, 60-63, 82, 99, 112, 124, 130-132, 143, 146-147, 158-163, 171-175, 195-215, 228, 243-247, 251, 256, 261, 266-302, 306-336, 339-344, 349-352, 355-356, 359-360, 364-365, 371-372, 378-379, 385-386, 391-420, 423-427, 430, 464, 472-483, 493-497, 500-528, 538-540, 543-564, 572-575, 581-589 /home/admin/.local/lib/python3.8/site-packages/botocore/httpsession.py 210 158 25% 39-41, 45-51, 61-88, 99-105, 109-113, 117-122, 126, 129-134, 137-139, 142-146, 169-195, 199-206, 209-219, 222, 225-234, 237-243, 246-251, 254-274, 277-281, 284-301, 304, 307-359 /home/admin/.local/lib/python3.8/site-packages/botocore/loaders.py 139 98 29% 127-134, 154, 166-175, 192-199, 222-238, 242, 246, 267-287, 310, 327-340, 374-389, 393-400, 419-424, 430-441, 455-456, 460-461 /home/admin/.local/lib/python3.8/site-packages/botocore/model.py 398 242 39% 42, 81-93, 118-126, 145-150, 161, 164, 167, 172, 178-188, 192-195, 199-206, 212, 218, 222, 228, 259-266, 269, 273, 277-281, 284, 288, 292-297, 301-305, 309, 313, 327-330, 334-337, 348-351, 355, 359, 363, 367-370, 374-379, 382-385, 394-397, 401, 404, 437-444, 448-451, 465, 469, 473, 477, 483, 487, 491-495, 500-505, 510-514, 520, 524-525, 529, 533, 537, 541, 544, 547, 551-556, 560, 564, 567, 570, 574-581, 584, 599-600, 603-616, 624-636, 643, 647, 688-691, 702-703, 712-719, 724-734, 737-745, 748-752, 755-762, 765-773, 776, 779-782, 793, 821-823 /home/admin/.local/lib/python3.8/site-packages/botocore/monitoring.py 221 149 33% 42-43, 47-48, 56-61, 73, 89, 92, 97, 104-110, 114-124, 127-129, 136-138, 141, 145-147, 150, 153, 171-173, 176, 179-181, 214-220, 229-235, 287-296, 342-343, 346-347, 362-370, 374, 380, 383, 386, 389-391, 394-408, 412-415, 419, 422, 426-433, 438, 442-444, 448-451, 456-460, 464-467, 470-472, 475-481, 484, 490, 493, 496-500, 503-508, 531-533, 542-550 /home/admin/.local/lib/python3.8/site-packages/botocore/paginate.py 369 304 18% 52-68, 72-79, 83-90, 94-101, 105, 127-136, 140-144, 158-161, 172-173, 178, 181-186, 193-207, 211, 216, 220-232, 236, 239-303, 321-329, 332, 335, 338-344, 349-357, 360-364, 370-397, 401-424, 427-440, 443-444, 448-490, 493-506, 513-534, 541-550, 557-566, 570, 573-576, 579-585, 588-591, 594-596, 599-604, 607, 617-618, 629-646, 668-669, 672-677 /home/admin/.local/lib/python3.8/site-packages/botocore/parsers.py 528 407 23% 135, 147, 150-151, 155, 165-174, 199-207, 214, 233-262, 265, 279-284, 289-291, 299, 302, 306, 310-312, 317-321, 324, 327-329, 334-336, 339-357, 360, 368-370, 373-399, 402-406, 413-421, 427-444, 447-458, 461-467, 471-474, 478, 482, 486, 490, 494, 504-517, 520, 523, 526-538, 541-542, 545-551, 557-560, 575-580, 583-588, 594-609, 612-619, 622, 625, 628-652, 655-656, 660-669, 675-685, 688-706, 709-726, 730-740, 747, 753, 759-761, 770-778, 781, 784-793, 799-800, 806-810, 813-818, 821-823, 826-836, 839-860, 864-879, 885-893, 900, 903-907, 915, 918-920, 925-933, 942-944, 960-970, 973, 986-1006, 1010-1011 /home/admin/.local/lib/python3.8/site-packages/botocore/regions.py 90 69 23% 58, 65, 85, 94-96, 99-102, 106-116, 119-136, 141-165, 169-173, 176-190, 193-195, 199 /home/admin/.local/lib/python3.8/site-packages/botocore/response.py 67 45 33% 44-46, 60-69, 76-87, 92, 97-100, 110-117, 124-127, 133-135, 141, 145-162 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/retries/adaptive.py 70 50 29% 14-35, 44-50, 53-54, 58-77, 90-96, 99-113, 117 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/base.py 6 2 67% 9, 27 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/bucket.py 70 48 31% 10, 13, 16, 24-32, 36, 40-56, 60, 64, 76-77, 80-100, 103, 106-114 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/quota.py 24 16 33% 12-16, 28-32, 46-53, 57 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/special.py 28 17 39% 23-27, 36-48 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/standard.py 200 132 34% 40-61, 71-73, 77-93, 108-129, 136-138, 172-189, 199-204, 215, 218, 223-224, 227, 230, 239-241, 257, 265, 268-272, 290-298, 301-310, 334-336, 341, 348, 351-354, 376-391, 396-398, 402-406, 421-422, 433, 439, 442, 458-460, 463-475, 478, 490-498 /home/admin/.local/lib/python3.8/site-packages/botocore/retries/throttling.py 25 15 40% 13-17, 20-21, 24-28, 35-38, 50 /home/admin/.local/lib/python3.8/site-packages/botocore/retryhandler.py 158 118 25% 52-58, 68, 73-77, 85-87, 93-118, 124-128, 132-144, 148-155, 173-174, 183-187, 219-225, 228, 231, 245-247, 250-263, 266-277, 282, 285-291, 296-297, 300-307, 312, 315-320, 326, 329-340, 359 /home/admin/.local/lib/python3.8/site-packages/botocore/serialize.py 357 275 23% 65-69, 117, 122-130, 135-139, 142, 145-147, 150-157, 162, 168-170, 174-186, 190-192, 200-222, 232-234, 237-243, 246-262, 265-278, 282, 285, 289-292, 295, 298, 314-322, 325-328, 335-357, 360-362, 365-379, 382-385, 388-397, 400, 403, 407, 428-476, 485-493, 502-519, 523-525, 533-569, 572-574, 577, 580-591, 597-599, 606-610, 613-615, 618-641, 644-652, 663-671, 677-682, 685-686, 689-690, 694-695 /home/admin/.local/lib/python3.8/site-packages/botocore/session.py 384 276 28% 102-131, 134-141, 144, 147, 151, 156, 161-165, 169, 173, 177-186, 189-192, 196, 200-215, 218-226, 230, 236-238, 242-245, 248-251, 261-282, 308-313, 316, 340-353, 365-388, 398, 408, 427, 440-443, 468-480, 489, 505-506, 509-512, 515-518, 524-534, 540, 548, 573-588, 602-616, 652, 685, 690, 693-694, 697-707, 713, 719, 722, 725, 800-855, 860-877, 880-884, 892-893, 915-925, 931-932, 935-945, 948-952, 955-959, 964-965, 968, 971-972, 975, 978, 981, 992-995, 1017-1018, 1032-1038, 1051 /home/admin/.local/lib/python3.8/site-packages/botocore/signers.py 216 174 19% 64-71, 75, 79, 83, 90, 120-162, 174-199, 221-242, 271-276, 315-316, 333-351, 354-355, 385-396, 401, 406, 427-457, 462, 506-533, 537, 561-598, 604, 671-719, 725-734 /home/admin/.local/lib/python3.8/site-packages/botocore/translate.py 21 16 24% 21-38, 42-55, 70-76 /home/admin/.local/lib/python3.8/site-packages/botocore/utils.py 1126 888 21% 181-184, 196, 207-214, 218-220, 226-229, 236-256, 262-267, 276-300, 310, 322-331, 337, 340-364, 367-395, 413-432, 435-436, 439-441, 444, 450-453, 456-459, 462-472, 482-513, 516, 523-528, 531-536, 539, 545, 552-558, 569-587, 592-595, 599-604, 611-619, 642-655, 671-676, 681-695, 710-716, 747-762, 775-783, 804-810, 827-843, 855-860, 875-880, 910, 922-923, 926-949, 952-958, 963-966, 971-974, 980-981, 993-1004, 1007, 1011-1016, 1030-1039, 1053-1060, 1077-1124, 1128, 1150-1159, 1170-1179, 1184-1187, 1191-1193, 1197-1212, 1222-1230, 1238, 1243-1250, 1253-1256, 1266-1343, 1358-1376, 1379-1381, 1388-1395, 1398, 1407-1413, 1435-1437, 1440, 1443-1451, 1454-1462, 1465-1470, 1480-1481, 1490-1499, 1515-1523, 1526-1527, 1533-1553, 1556-1567, 1570, 1573-1600, 1608-1615, 1618-1619, 1622-1633, 1636-1662, 1669-1671, 1674-1679, 1682-1688, 1691-1697, 1705-1732, 1738-1744, 1750-1766, 1777-1785, 1788, 1791-1802, 1805, 1808, 1811-1835, 1838-1839, 1847-1853, 1856-1858, 1861-1862, 1865-1876, 1879-1883, 1886, 1889-1891, 1894-1906, 1909-1918, 1921, 1924-1925, 1928-1933, 1936-1942, 1945-1951, 1954-1957, 1960, 1967-1969, 1972, 1978-1985, 1988-1997, 2000, 2003-2013, 2016-2018, 2022-2031, 2037-2043, 2046-2051, 2054-2063, 2069-2075, 2078-2083, 2095-2100, 2111-2112, 2115-2119, 2125-2127, 2138-2139, 2142-2156, 2159-2180, 2183, 2187-2190, 2205-2211, 2221-2232, 2236-2240, 2244-2245, 2249-2254, 2259-2263, 2268-2269, 2272-2273, 2278-2280, 2283, 2286-2296 /home/admin/.local/lib/python3.8/site-packages/botocore/validate.py 164 114 30% 46-49, 55-56, 59-64, 71-84, 89, 92-94, 97-100, 103-128, 132-137, 140, 160-162, 165-166, 169-173, 179-182, 187-202, 215, 219-222, 226-231, 235, 238-244, 250, 254, 260, 266-269, 273-279, 284-285, 288-294 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/exceptions.py 26 6 77% 21-27 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/requests/packages/urllib3/exceptions.py 67 15 78% 17-18, 22, 28-29, 33, 73-78, 85-87, 135-138 /home/admin/.local/lib/python3.8/site-packages/botocore/vendored/six.py 444 211 52% 49-72, 98-99, 112, 118-121, 131-133, 145, 154-157, 192-193, 203, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /home/admin/.local/lib/python3.8/site-packages/botocore/waiter.py 175 140 20% 44-73, 79-82, 87, 90-93, 113-121, 124-125, 132-136, 147-154, 158-162, 167-171, 175-186, 197-208, 212-219, 222-239, 242-259, 262-271, 274-284, 303-307, 310-367 /home/admin/.local/lib/python3.8/site-packages/cached_property.py 93 61 34% 14-15, 30-37, 40-47, 57-59, 62-74, 85-91, 94-95, 98-115, 118, 121, 124-128, 143-144, 147-148 /home/admin/.local/lib/python3.8/site-packages/cffi/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/cffi/api.py 544 341 37% 8-11, 52-59, 82, 97-98, 115-117, 121-123, 129-130, 133-135, 147, 160, 166, 169, 174, 190, 200, 203-211, 217-221, 227-229, 238-240, 284-291, 299, 318, 335, 361-365, 382, 392-403, 411-419, 431, 454-473, 476, 478, 483, 486-487, 495-508, 511-515, 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350-388 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/constant.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/legacy.py 19 14 26% 22-50 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/models.py 174 110 37% 20-34, 37-43, 49-62, 66, 70-72, 75, 78-86, 90, 97-103, 107, 111, 119, 127-147, 151, 155-157, 161, 165, 172, 176, 180, 184-192, 201, 208-212, 219, 229, 232, 239-246, 249, 252, 259-272, 278-280, 286, 308-318, 322, 337 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/utils.py 214 158 26% 24-28, 40-46, 54-60, 65-69, 74-78, 83-93, 98-108, 113-118, 123-128, 133, 137-139, 144-149, 154-159, 164-169, 174-179, 184-189, 194, 199, 212-237, 245, 266-276, 280, 284-296, 300-310, 314-334, 342, 353-358, 372-414 /home/admin/.local/lib/python3.8/site-packages/charset_normalizer/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/colorama/__init__.py 4 0 100% 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82-94, 98, 102-104, 107, 110-113, 116, 121-133, 137, 143-145, 148, 151-154, 157, 162-165, 169, 172, 175-178, 181, 186-189, 193, 196, 199-202, 205, 210-225, 229, 232-237, 242, 245-248, 251, 256-262, 266, 270, 273, 278-281, 284 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/name.py 232 142 39% 64-87, 91-106, 118-164, 168, 172, 180, 191-197, 200-203, 206, 209, 214-225, 230, 241, 247-250, 253, 256, 259, 262, 268, 274, 282-293, 304, 319, 327, 331, 334, 337-340, 345, 348-350, 353, 356-357, 388-391, 394, 397-399, 402-404, 407-412, 423-429, 432-437, 440-460 /home/admin/.local/lib/python3.8/site-packages/cryptography/x509/oid.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/__init__.py 16 2 88% 18-19 /home/admin/.local/lib/python3.8/site-packages/cv2/data/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/cv2/version.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dateutil/__init__.py 13 4 69% 6-7, 17, 24 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222-225, 230-235, 240-244, 249-253, 258-281 /home/admin/.local/lib/python3.8/site-packages/dill/logger.py 124 79 36% 123, 126, 129-135, 137-168, 182-184, 187-188, 192-209, 257, 263-266, 268-278, 280-285 /home/admin/.local/lib/python3.8/site-packages/dill/objtypes.py 9 1 89% 18 /home/admin/.local/lib/python3.8/site-packages/dill/pointers.py 60 50 17% 29-34, 44-51, 67-74, 84-115 /home/admin/.local/lib/python3.8/site-packages/dill/session.py 266 226 15% 40-55, 66-75, 78-117, 120-128, 221-262, 266-267, 273, 275, 277, 279, 281, 283-292, 296-304, 308-327, 431-507, 511-512, 571-603 /home/admin/.local/lib/python3.8/site-packages/dill/settings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/dill/source.py 613 574 6% 37-40, 45-49, 54-102, 115-258, 271-329, 345-346, 368-439, 444, 448-463, 468-473, 478, 483-508, 513-521, 525-529, 537-546, 552-560, 571-599, 604-624, 640-666, 679-713, 727-766, 791-827, 833-881, 891-921, 941-1001, 1006, 1009, 1011 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/home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/base.py 8 2 75% 8, 12 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/binary.py 15 5 67% 15, 26, 36, 48-49 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/compatibility.py 24 13 46% 13, 19, 22-39 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/der.py 149 110 26% 27-28, 32-43, 47-53, 57, 61, 65, 69-74, 78-89, 93-111, 115-121, 125-131, 135-144, 148-152, 156-164, 168-180, 184-194, 198-207, 211-227, 231-232, 239 /home/admin/.local/lib/python3.8/site-packages/ellipticcurve/utils/integer.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/filetype/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/filetype/filetype.py 21 12 43% 28, 45-46, 63-64, 79-82, 95-98 /home/admin/.local/lib/python3.8/site-packages/filetype/helpers.py 23 12 48% 23-26, 41-44, 76, 92, 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76, 91-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/table_builder.py 118 99 16% 49, 53, 57-72, 84-86, 89-119, 122-181, 186-188, 191-223 /home/admin/.local/lib/python3.8/site-packages/fontTools/colorLib/unbuilder.py 41 32 22% 6-10, 17-21, 26-34, 37-38, 41-58, 62-81 /home/admin/.local/lib/python3.8/site-packages/fontTools/config/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/designspaceLib/__init__.py 1422 1195 16% 49-56, 64, 68-70, 77-78, 81, 86-95, 105-109, 118-120, 185-296, 311, 315, 322, 329, 339-345, 379-390, 402, 411-421, 431-446, 525-650, 661, 665, 669, 672, 675, 678, 681, 684, 687, 690, 720-732, 744-752, 774-785, 794, 799-812, 830-869, 925-945, 953, 968-972, 976-982, 1031-1052, 1064, 1074-1076, 1116-1137, 1152-1156, 1161, 1187-1208, 1215, 1223, 1249-1271, 1286-1299, 1315-1320, 1338, 1342, 1346, 1350, 1353-1356, 1359-1409, 1421-1439, 1443-1460, 1463-1465, 1469-1497, 1500-1527, 1532-1548, 1551-1555, 1560-1568, 1577-1596, 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35-37, 40-43, 64-74, 78-88, 92-107, 111-147 /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/encodings/codecs.py 62 50 19% 10-18, 21-36, 39, 42, 45-56, 109-132 /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/feaLib/ast.py 1046 766 27% 81-84, 146-153, 161-163, 166, 172, 175, 190-192, 195, 202, 207, 210, 217-219, 223, 226, 233-237, 241, 244-250, 254, 258, 265-269, 275-279, 284-288, 296-298, 302, 305, 313-315, 319, 322, 329-331, 334-337, 344-345, 351-352, 355-356, 368-369, 372, 379-380, 386-395, 398-404, 412-414, 417-419, 422-425, 432-433, 437-439, 442-448, 455-456, 459-462, 469-471, 475, 478, 489-492, 496-500, 503, 527-529, 533-545, 549, 552-553, 580-583, 587, 590, 602-604, 608-614, 617-629, 649-652, 655-666, 673-674, 677-681, 688-690, 694-695, 698, 718-726, 730-733, 738-758, 776-784, 788-791, 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7 4 43% 69-72 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/etree.py 263 257 2% 47-478 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/filenames.py 76 64 16% 99-134, 168-191, 221-239, 243-246 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/fixedTools.py 35 20 43% 81, 109-110, 137-138, 157-158, 188-190, 212-213, 231-239, 251-253 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/intTools.py 9 2 78% 9, 25 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/loggingTools.py 240 173 28% 57-76, 79-86, 134-185, 192-226, 294-296, 306, 312, 316-319, 327-336, 340-342, 348-357, 370, 374-375, 384, 387, 390, 421-423, 426-434, 439-444, 447-456, 459-464, 467, 470-478, 511-514, 519, 528-533, 541-543 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/plistlib/__init__.py 259 182 30% 66-78, 82, 103-105, 109, 112, 117-122, 125, 131-142, 188-201, 204-207, 210-212, 215, 218-220, 225-238, 241-243, 250-252, 256-258, 262-264, 268-270, 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149, 153-155 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/timeTools.py 32 19 41% 48-56, 60, 64-69, 74-77, 81, 85-88 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/transform.py 126 78 38% 69-75, 175-177, 189-190, 202-204, 215-216, 227, 241-243, 255-259, 271-273, 285-287, 309-311, 332-338, 350, 354, 376, 379, 393, 405-407, 430-460, 480-488, 492-495 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/treeTools.py 26 24 8% 12-45 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/vector.py 80 42 48% 22-30, 33, 36-41, 44-46, 49, 52, 57, 60, 63, 68, 71, 74, 77, 80, 83-86, 89, 92, 97, 101, 105, 110-111, 116-120, 124-129, 133, 139, 143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/visitor.py 88 42 52% 22, 54, 58-70, 82-94, 98, 102-103, 107-108, 113, 131-143 /home/admin/.local/lib/python3.8/site-packages/fontTools/misc/xmlWriter.py 146 118 19% 20-55, 58, 61, 64-65, 69, 73, 80, 84, 88-94, 97-102, 105-111, 114-116, 119-123, 126-130, 133-144, 147, 150-151, 154-167, 171-176, 180-182, 188-195, 199-204 /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/otlLib/builder.py 1114 926 17% 55-59, 99-123, 130-137, 140, 149, 153, 156, 165-170, 173-177, 180-184, 187-191, 194-198, 201-208, 218, 249-250, 253, 262-265, 268, 271, 279, 284, 287, 293-296, 302-306, 309-319, 322-329, 334, 339-346, 349-359, 368-429, 432-475, 478-547, 550-559, 562-580, 583, 593-599, 612-621, 624-633, 636-640, 643-647, 652-673, 676, 712-714, 718-726, 757-759, 762-774, 778-787, 814-815, 818, 827-830, 833, 860-861, 864, 867-868, 871, 892-893, 896, 913-914, 923-924, 956-958, 961, 968-970, 979-997, 1032-1034, 1037, 1044-1046, 1055-1065, 1096-1098, 1101, 1108-1110, 1119-1137, 1167-1168, 1171, 1180-1190, 1194, 1220-1221, 1224, 1233-1234, 1237, 1240, 1255-1259, 1272-1286, 1290, 1294-1295, 1298-1304, 1328-1331, 1344, 1357-1372, 1375, 1385, 1398-1423, 1444-1446, 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140-149, 158-172, 202-206, 210-212, 216-221, 226, 230-260, 264-292, 301-302, 309, 317-452 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/basePen.py 178 122 31% 67, 71, 92, 109, 115, 121, 133, 147, 155, 158, 161, 164, 167, 170, 173, 176, 209-210, 214-224, 228, 240-241, 246, 249, 252, 257, 260, 267-274, 282, 285-286, 289-290, 293-294, 297-298, 301-327, 330-355, 368-390, 403-412, 419, 422, 425, 431, 435-444 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/boundsPen.py 56 41 27% 25-27, 30-31, 34-36, 39-47, 50-51, 54-59, 62-66, 83-90, 93-100 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/filterPen.py 55 31 44% 8, 58-59, 62-63, 66-67, 70-71, 74-75, 78-79, 82-83, 95-96, 99-100, 103-104, 107-111, 126, 155, 158, 161, 164 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/pointPen.py 292 246 16% 36, 40, 52, 62, 78, 91, 94-96, 124, 127-172, 177-179, 192-194, 197-259, 262-264, 274-278, 281-285, 288-289, 292-294, 297-303, 306-316, 319-331, 334-337, 340-342, 352-354, 357-394, 397-402, 405-407, 412-416, 419-423, 428-432, 446-448, 451-498, 501-505, 508-511, 516-520, 523-525 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/recordingPen.py 61 35 43% 22-23, 50, 53, 56, 59, 62, 65, 68, 71, 74, 77, 135, 138-140, 143, 148-150, 153-155, 160-162, 167-168, 172-179 /home/admin/.local/lib/python3.8/site-packages/fontTools/pens/transformPen.py 52 35 33% 18-25, 28, 31, 34, 37-41, 44-45, 48, 51, 54-55, 86-92, 95, 100-101, 105-111 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/__init__.py 2014 1674 17% 451-454, 466, 470-471, 477, 483, 489-491, 497, 503, 512-514, 520-534, 540, 545, 550-553, 558-560, 565-570, 575, 580-585, 590, 601-611, 616-625, 630-646, 651, 656-659, 664-674, 679-706, 711-752, 757-773, 778-786, 791-799, 804-810, 815-837, 842-850, 855-880, 885-894, 899-921, 926-934, 951, 968, 983, 990, 1000, 1010-1160, 1165-1272, 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2916-2964, 2970, 3084-3131, 3134-3137, 3140-3214, 3228-3234, 3237-3245, 3248-3273, 3276-3433, 3436-3459, 3462-3492, 3495-3498, 3501-3504, 3509-3536, 3541-3547, 3551-3563, 3567-3574, 3578, 3582-3583, 3589-3724, 3739 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/cff.py 345 294 15% 11-12, 15-25, 30-48, 52-68, 73-89, 94-132, 137-144, 151-185, 190-195, 198-199, 202-203, 213-229, 236-240, 243-264, 267-269, 272-274, 277-278, 281-282, 285-286, 289-290, 293-295, 298-300, 303-317, 320-324, 327-353, 358-381, 385-388, 396, 401-464, 469-536 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/svg.py 141 114 19% 10-13, 36, 43, 49-57, 65-87, 95-105, 112-124, 132-156, 160-164, 169-174, 179-190, 195-251 /home/admin/.local/lib/python3.8/site-packages/fontTools/subset/util.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/__init__.py 50 36 28% 21-23, 54-119, 123 /home/admin/.local/lib/python3.8/site-packages/fontTools/ttLib/sfnt.py 378 307 19% 33-44, 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/home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/base.py 32 10 69% 44, 53, 67, 87, 104-109, 124-127 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/es256.py 67 34 49% 48, 53-73, 90-101, 117-118, 123, 127-132, 156-160, 164-170, 174-175 /home/admin/.local/lib/python3.8/site-packages/google/auth/crypt/rsa.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/environment_vars.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/google/auth/exceptions.py 31 6 81% 22-24, 28, 58, 70 /home/admin/.local/lib/python3.8/site-packages/google/auth/iam.py 42 21 50% 84-86, 90-116, 126, 130-131 /home/admin/.local/lib/python3.8/site-packages/google/auth/jwt.py 237 160 32% 89-115, 120-127, 143-168, 184-185, 201-228, 257-316, 395-406, 424-426, 443-444, 458-461, 491-493, 514-517, 528, 543-560, 570, 574, 579, 584, 589, 637-648, 666-668, 685-686, 700-703, 729-731, 748-751, 763, 779, 790-806, 821-827, 841, 858-864, 868, 873, 878 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439-450, 494-508 /home/admin/.local/lib/python3.8/site-packages/google/oauth2/service_account.py 249 165 34% 174-197, 215, 243-246, 260-263, 268, 273, 282, 285-299, 303-306, 321-330, 334-338, 350-352, 366-370, 374-376, 380-382, 393-415, 420, 423-425, 429-452, 461-490, 496, 501, 506, 510-516, 599-621, 639-643, 661-664, 678-681, 684-695, 709-711, 730-739, 743-745, 749-751, 762-782, 803-809, 820-828, 833, 837, 842, 847 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/any_pb2.py 16 4 75% 31-34 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor.py 404 261 35% 66-73, 80-82, 85, 88, 95-97, 138-143, 151-155, 163-181, 209-220, 232-238, 327-369, 376-379, 397, 406, 558, 575-607, 616-618, 635-638, 684-697, 706, 742-750, 781-790, 827-828, 833-845, 856, 865, 892-893, 903-913, 925-931, 977-980, 984, 991-1011, 1019, 1028-1029, 1034-1050, 1055-1058, 1063-1075, 1096-1179 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_database.py 57 42 26% 50-51, 63-85, 104, 128-141, 145, 149, 152-158, 171-177 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/descriptor_pool.py 465 393 15% 75-80, 99, 104-107, 111, 140-155, 165-193, 202, 216-221, 227, 240-246, 252, 264-287, 293, 303-308, 314, 329-356, 361, 374-379, 392-394, 409-423, 439-450, 465-499, 514-517, 532-535, 549-552, 566-569, 583-599, 618-622, 637-646, 655-674, 688-691, 705-708, 722-731, 745-826, 843-920, 939-972, 995-1005, 1037-1056, 1067-1132, 1145, 1169-1188, 1204-1209, 1229-1234, 1246-1250, 1263-1272, 1276, 1285 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/api_implementation.py 31 9 71% 44-45, 48, 65-67, 86, 102, 107, 112 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/containers.py 328 229 30% 62-63, 67, 71, 76, 79, 82, 88-90, 93, 114-115, 119-121, 125-127, 132-146, 152-153, 157-158, 162-164, 168-174, 178, 182-186, 190-191, 195-196, 200-206, 231-232, 238-243, 247-252, 256-261, 268-276, 282, 286-287, 291-293, 297, 301-302, 306-307, 311-316, 340-344, 347-353, 358-359, 365-368, 371-374, 377-378, 381, 384, 387, 390-391, 396-398, 402-403, 406, 430-434, 437-446, 460, 466-469, 472-473, 476, 479-481, 484, 487, 490, 494-499, 506-508, 512-513, 516, 527-530, 534, 537-540, 548-550, 553-557, 562-564, 568-570, 574-576, 587, 590-599, 602, 605-608, 611-613, 616-617, 620-623, 626-636, 639-643 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/decoder.py 499 420 16% 108-120, 131-144, 173-179, 196-243, 257-258, 281-283, 309-328, 353-368, 375-508, 547-593, 600-634, 640-681, 687-731, 752-832, 839-876, 887-892, 897-900, 905-906, 912-916, 923-927, 933-944, 950-967, 973, 979-982, 988-989, 995, 1024-1025 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/encoder.py 475 391 18% 82-91, 96-106, 113, 129-148, 158-177, 187-203, 231-247, 253-269, 275-287, 293-308, 326-334, 346-364, 379-381, 394-402, 442-466, 476-500, 516-538, 559-566, 569-578, 582-615, 650-681, 687-705, 711-727, 733-748, 754-769, 787-801, 820-829 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/enum_type_wrapper.py 33 19 42% 53-64, 69-73, 83, 103, 108-114 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/type_checkers.py 121 59 51% 61, 71-76, 80, 92-101, 123-132, 142, 153-155, 157, 164, 172, 175-181, 184, 195-217, 220, 261-272, 275 /home/admin/.local/lib/python3.8/site-packages/google/protobuf/internal/well_known_types.py 447 337 25% 68-72, 76-80, 85, 89, 108-126, 139-190, 194, 198, 202, 207, 212, 216-217, 221-222, 226-227, 231-232, 236, 249-250, 267-288, 301-321, 326, 330-331, 335-336, 340, 344, 349, 355, 361-362, 366, 372, 378-382, 386-395, 406-411, 421-424, 428-433, 437-440, 444-446, 458-459, 463-467, 471-477, 492-493, 499-508, 513-516, 522-546, 551-561, 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/home/admin/.local/lib/python3.8/site-packages/googleapiclient/http.py 672 543 19% 43-44, 90-147, 170-229, 243-244, 253-256, 269-270, 279-282, 318, 326, 334, 342, 355, 367, 376, 389-396, 405, 419-425, 469-478, 486, 494, 502, 510, 523-524, 536, 545, 549, 592-601, 606-607, 616, 620-621, 658-659, 695-713, 734-780, 801-804, 816-820, 852-874, 896-939, 951, 992-1093, 1109-1126, 1133-1141, 1146-1149, 1162, 1210-1247, 1259-1277, 1290-1296, 1313-1319, 1331-1367, 1379-1395, 1405-1408, 1440-1453, 1468-1525, 1544-1606, 1624-1630, 1638, 1681-1682, 1700-1720, 1732-1744, 1755-1759, 1762, 1794-1796, 1808-1826, 1848-1877, 1898-1930, 1944-1962 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/mimeparse.py 56 41 27% 45-56, 73-83, 95-120, 133, 147-149, 166-177, 181-183 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/model.py 170 103 39% 38, 51, 79, 94, 118-130, 152-182, 193-205, 209-215, 230-241, 252, 264, 284, 287-293, 296-307, 311, 327, 331, 347, 351, 375, 378, 381, 385, 409-429 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/sample_tools.py 31 22 29% 58-108 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/schema.py 110 86 22% 81-84, 99-114, 127, 142-145, 158, 167, 184-200, 208, 216, 225-232, 236, 240, 251-302, 316-317 /home/admin/.local/lib/python3.8/site-packages/googleapiclient/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/httplib2/__init__.py 914 737 19% 59-61, 151-188, 192-194, 202-217, 228-229, 233-245, 258-274, 281, 290-292, 296-304, 342-386, 390-412, 416-429, 433-471, 475-478, 482, 498-502, 505-506, 510-511, 516, 526, 529, 532, 535, 538, 541, 544, 547, 552, 557, 567-579, 583-615, 618-630, 639-678, 682-689, 705-708, 721, 726-730, 740-765, 770, 791-794, 797-805, 808-811, 814-816, 824, 827, 830-832, 840, 843-845, 877-881, 900, 911, 914, 918-929, 932, 942-949, 955-979, 994-998, 1002-1066, 1093-1114, 1118-1202, 1272, 1313-1317, 1320-1327, 1330-1331, 1337-1341, 1346, 1351, 1356-1357, 1360-1431, 1439-1510, 1513, 1542-1751, 1775-1793, 1796-1799 /home/admin/.local/lib/python3.8/site-packages/httplib2/auth.py 40 17 58% 40-49, 54-69 /home/admin/.local/lib/python3.8/site-packages/httplib2/certs.py 29 9 69% 10-11, 17, 30-33, 35, 38, 42 /home/admin/.local/lib/python3.8/site-packages/httplib2/error.py 25 3 88% 10-12 /home/admin/.local/lib/python3.8/site-packages/httplib2/iri2uri.py 41 30 27% 49-57, 64-72, 76-124 /home/admin/.local/lib/python3.8/site-packages/httplib2/socks.py 244 201 18% 44, 127, 139-142, 155-162, 169-175, 181-183, 190-206, 209-210, 241, 256-352, 358, 364, 371, 378-422, 429-467, 477-518 /home/admin/.local/lib/python3.8/site-packages/imgaug/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/base.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/batches.py 266 116 56% 24, 168, 285-311, 374, 380, 386, 392, 398, 415, 431, 583-610, 624, 630, 717, 751, 771-785, 803-816, 853-888, 943-954, 979-981, 997, 1018-1029, 1051-1068, 1081-1088 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/bbs.py 484 365 25% 49-58, 73-76, 91, 106, 121, 136, 148, 160, 172, 184, 196, 214-218, 249-253, 282, 318-322, 355, 381-387, 406, 433-437, 459-464, 493-501, 521-523, 548-552, 581-585, 592, 614-628, 647, 673-677, 721-723, 784-795, 843-895, 946-957, 1003-1085, 1101-1103, 1122-1124, 1158-1173, 1201-1203, 1228-1244, 1277, 1319, 1332, 1345, 1348, 1351, 1382-1383, 1397, 1411, 1425, 1439, 1451, 1473-1481, 1500, 1529-1544, 1574-1580, 1601-1606, 1620, 1652-1670, 1694-1695, 1735-1748, 1773-1778, 1800, 1825, 1847, 1854, 1870-1876, 1887, 1913-1915, 1958-1960, 1974-1989, 2015-2028, 2042-2045, 2066-2072, 2094-2100, 2113, 2128, 2141, 2144, 2147, 2158-2165, 2169-2187, 2190, 2194-2195, 2203-2227, 2230-2246, 2251-2258, 2261-2271 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/heatmaps.py 143 116 19% 45-96, 113-125, 156-187, 221-254, 280-286, 323-333, 381-396, 413-414, 435-436, 446, 467-475, 494-496, 536-537, 580-584, 614-659, 671, 682 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/kps.py 364 260 29% 37-67, 100-102, 114, 126, 140, 154, 184-186, 213, 235-239, 265, 287-289, 308, 347-408, 456-487, 512-526, 554, 575, 601, 604, 647, 661, 673, 685, 718-726, 744, 785-790, 819, 845, 863, 879, 901-903, 922, 936, 975, 996-1006, 1077-1097, 1139-1184, 1214-1228, 1280-1334, 1351, 1374-1384, 1405-1411, 1452, 1467, 1480, 1483, 1486 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/lines.py 517 418 19% 48-67, 80-82, 94, 106, 122, 138, 153-155, 170-172, 192-198, 211-213, 240-262, 285-288, 313, 343-344, 371, 400-409, 431-433, 456-463, 495-502, 520-614, 634-671, 697-699, 742-744, 772-778, 812-823, 854-864, 907-923, 967-1052, 1097-1104, 1176-1210, 1266-1305, 1325-1327, 1356-1360, 1372-1373, 1385-1390, 1405-1406, 1443-1444, 1483-1484, 1520-1539, 1563-1565, 1587, 1609, 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993-1008, 1031-1041, 1062-1069, 1089, 1101-1103, 1125, 1138-1142, 1157-1159, 1178-1181, 1208-1223, 1250, 1297-1306, 1340-1342, 1364, 1385, 1400, 1413, 1416, 1419-1423, 1455-1456, 1470, 1484, 1496, 1517-1525, 1543, 1637-1653, 1678-1682, 1702, 1725, 1747, 1777-1781, 1806, 1831-1833, 1876-1878, 1896-1898, 1916, 1930-1932, 1964-1988, 2003-2009, 2032-2047, 2072-2078, 2104-2110, 2123, 2138, 2151, 2154, 2157, 2164-2182, 2188-2230, 2233-2281, 2284-2299, 2303-2317, 2321-2328, 2331-2392, 2397-2407, 2413-2526, 2531-2573, 2579-2589, 2592-2760, 2777-2782, 2806-2829 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/segmaps.py 124 101 19% 21, 103-167, 195-205, 211, 236-255, 312-381, 418-421, 466-476, 482, 505-507, 536-540, 568-572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmentables/utils.py 114 88 23% 12, 25-37, 57-59, 90-111, 139-142, 167-173, 204-218, 252-270, 290-296, 323-334, 339-342, 348-361 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/arithmetic.py 791 668 16% 101-118, 130-161, 165-204, 247-261, 274-283, 298-325, 383-400, 411-442, 448-492, 548-565, 571-583, 587-628, 686, 754-781, 816-847, 877-893, 943-1004, 1038, 1132-1186, 1190-1193, 1199-1202, 1207-1211, 1236-1252, 1259-1261, 1268-1281, 1287-1309, 1335, 1369, 1407-1470, 1552-1559, 1564-1607, 1611, 1698-1705, 1710-1729, 1733, 1836-1845, 1959-1968, 2075-2081, 2167-2174, 2179-2221, 2225, 2311-2318, 2323-2353, 2357, 2365-2373, 2554-2570, 2576-2587, 2591-2627, 2631-2663, 2680-2698, 2703, 2792-2794, 2803-2834, 2993-3010, 3113-3128, 3132-3165, 3171-3209, 3214, 3301-3315, 3319-3353, 3357-3359, 3364-3366, 3371, 3483-3491, 3496-3537, 3541, 3612, 3675, 3816-3831, 3905-3913, 4035-4055, 4132-4139, 4259-4279, 4414-4431, 4436-4460, 4464-4485, 4496, 4505-4510, 4579, 4664-4665, 4742-4748, 4754-4765, 4769 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/artistic.py 105 84 20% 99-157, 164-170, 175-189, 194-198, 203-206, 211-221, 226-239, 244-246, 365-381, 385-396, 400-401, 413 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/base.py 14 7 50% 21-27, 43, 49 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blend.py 584 447 23% 38-43, 111-211, 216-242, 247-258, 403-422, 426-472, 476, 481, 486, 491-498, 616-648, 652-695, 704-718, 724-785, 789, 794, 799, 804-810, 958-962, 970, 1160-1190, 1411-1442, 1581, 1704, 1833, 1962, 2064, 2196, 2325, 2407-2409, 2418-2423, 2429-2451, 2562-2578, 2586-2592, 2598, 2607-2626, 2667-2684, 2690-2694, 2701-2703, 2708-2735, 2740-2744, 2752-2762, 2773-2777, 2783, 2792-2801, 2842-2881, 2942, 2982, 3041, 3081, 3134-3142, 3153-3158, 3164-3175, 3205-3242, 3275, 3291, 3305, 3315-3320, 3348-3359, 3434-3450, 3462-3469, 3475-3490, 3518-3528, 3598-3614, 3626-3637, 3643-3658, 3686-3699, 3724-3725, 3734-3740, 3758, 3782, 3809, 3842 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/blur.py 259 182 30% 174-181, 186-215, 219, 221, 223, 225, 242, 263, 265, 329-367, 372, 376, 461, 473, 570-616, 622-686, 690, 768-781, 787-815, 819, 932-942, 949-975, 979, 1075-1088, 1098-1101, 1106-1134, 1213-1220, 1227-1236, 1240-1241, 1251 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/collections.py 50 33 34% 192-218, 233, 254-293, 340-341 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/color.py 642 462 28% 69-72, 240-310, 374-395, 405-407, 414, 436-454, 459-853, 888-950, 983, 994, 1063-1069, 1073-1093, 1096-1100, 1104, 1108, 1111, 1226-1234, 1238-1266, 1270-1277, 1282-1285, 1289-1293, 1298, 1303, 1307, 1396-1402, 1414, 1497, 1571, 1666-1675, 1679-1688, 1692-1712, 1716-1744, 1747-1751, 1755, 1759, 1762, 1891-1974, 2048, 2117, 2196-2204, 2380-2398, 2401-2438, 2442-2483, 2501-2511, 2516-2521, 2525, 2530-2543, 2547-2554, 2558-2564, 2572-2582, 2653, 2726, 2858-2905, 2908-2913, 2917-2939, 2943, 3021, 3076-3083, 3087-3095, 3100, 3115-3126, 3129-3139, 3143-3153, 3157-3210, 3218, 3228-3238, 3380, 3396, 3399, 3409, 3484-3526, 3663, 3679, 3682, 3824, 3836, 3858, 3889, 3957-3974, 3993-3995, 4015-4017, 4023, 4031, 4039-4053, 4081, 4133-4136, 4165 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/contrast.py 303 239 21% 37-45, 49-90, 94, 149-172, 233-258, 318-340, 388-421, 489-493, 589-599, 676-681, 754-761, 788-803, 806-898, 902, 1001-1012, 1017-1082, 1086, 1265-1275, 1280-1309, 1313-1315, 1388, 1394-1420, 1424, 1541-1549, 1554-1583, 1587-1588 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/convolutional.py 149 115 23% 126-143, 150-234, 238, 313-322, 330-331, 334-353, 424-433, 441-442, 445-464, 521-527, 535, 538-556, 654-664, 672-673, 676-717 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/debug.py 470 319 32% 42-77, 138-140, 145, 150, 154-159, 171, 176, 181-184, 188-191, 203-205, 210, 215, 219-250, 255-256, 271-272, 277, 282, 286-294, 305, 314, 323, 332, 340-341, 358-359, 367-373, 470-496, 502, 509, 515-527, 533-558, 564-593, 599-611, 617-671, 677-690, 696-708, 714-756, 762-825, 832-843, 849-850, 863, 868, 873, 878-882, 887, 892-894, 899-901, 906-908, 913-915, 920, 925-926, 931-932, 937, 942-943, 948-949, 953-957, 961-965, 970, 975, 980, 985-987, 992, 997, 1002, 1007-1010, 1051, 1055-1056, 1060-1061, 1071-1075, 1079-1080, 1086, 1123-1124, 1128-1130, 1170-1174, 1178-1193, 1256-1266, 1273-1274 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/edges.py 94 68 28% 93-101, 110-114, 117-159, 162, 327-375, 382-415, 419-468, 472, 476 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/flip.py 114 92 19% 722-724, 728, 734-761, 805, 811, 817, 872-875, 879-925, 929, 984-987, 991-1039, 1043 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/geometric.py 1751 1369 22% 92-96, 102, 106-120, 129, 139, 143-150, 158, 162, 169, 185-188, 215-244, 267, 269, 298-312, 315, 317, 323-351, 399-443, 475-508, 543-582, 607, 611, 639, 672, 681, 1178-1181, 1224-1229, 1253-1268, 1279-1284, 1309-1314, 1340, 1345, 1366-1371, 1392, 1414, 1417, 1422-1424, 1427, 1434-1473, 1479, 1489, 1503, 1513-1515, 1536, 1607, 1684, 1771-1774, 1862-1865, 1941, 2016, 2091, 2405-2571, 2576-2583, 2591-2676, 2679-2691, 2694-2707, 2711-2774, 2778, 2783, 2788, 2793, 2797-2841, 2849-2854, 2857-2860, 3009-3043, 3047-3081, 3085-3128, 3133-3169, 3174-3242, 3245-3271, 3287-3354, 3358, 3365-3369, 3551-3586, 3592-3621, 3632-3681, 3685-3753, 3758-3817, 3821-3847, 3852-3962, 3972-3988, 3993-4011, 4015, 4021-4026, 4239-4269, 4273-4275, 4281-4293, 4298-4304, 4310-4358, 4363-4379, 4385-4433, 4438-4496, 4501-4503, 4509-4511, 4516-4518, 4522, 4527-4554, 4672-4793, 4901-4911, 4914, 4919-4943, 4948-4966, 4971-4991, 4996-5029, 5033, 5165-5168, 5172-5204, 5209-5238, 5243-5278, 5283, 5288, 5293, 5298, 5304, 5310, 5316, 5321, 5326-5328, 5333-5335, 5340, 5345, 5350, 5355, 5360-5413, 5419-5476, 5481-5505, 5511-5532, 5537-5560, 5566-5589, 5594-5608, 5613-5629, 5635-5654, 5667-5707, 5712, 5717, 5721-5725, 5729-5733, 5849-5862, 5866-5935, 5939-5955, 5961-5970, 5976-5986, 5991-5996, 6001-6007, 6011, 6018-6021 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/imgcorruptlike.py 197 121 39% 96-112, 144-212, 243-257, 297, 329, 361, 393, 425, 457, 470-515, 550, 582, 614, 646, 678, 710, 742, 774, 806, 838, 870, 902, 934, 1009-1014, 1020-1029, 1033-1037, 1042, 1098, 1157, 1216, 1275, 1334, 1393, 1452, 1511, 1570, 1629, 1688, 1747, 1806, 1865, 1924, 1983, 2042, 2101, 2164, 2171-2177 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/meta.py 1062 774 27% 47, 53, 59-61, 68-72, 77-100, 108-117, 122-125, 130-142, 147-149, 153-157, 166-173, 207-210, 215, 219, 296, 299-304, 313-314, 320, 370-528, 550, 598-602, 610-616, 622-624, 626-632, 649, 654-659, 668-670, 713-737, 762-771, 821-822, 874, 901, 940, 966, 1010, 1071, 1116, 1180, 1246, 1315, 1364, 1406, 1449, 1479, 1524-1529, 1558, 1592-1596, 1631-1656, 1682-1687, 1933-1934, 1945-1957, 1984, 1995-2000, 2003, 2081-2082, 2139-2186, 2213-2214, 2252-2256, 2272-2287, 2296, 2357-2373, 2393-2397, 2443-2449, 2479-2487, 2552-2613, 2627, 2664, 2686-2696, 2740-2757, 2780, 2805-2813, 2871-2888, 2897, 2935-2947, 2958, 2975, 2978, 2981-2983, 3094, 3100, 3107, 3119, 3132-3137, 3141, 3152, 3156, 3159-3164, 3287-3315, 3319-3343, 3346-3355, 3358-3362, 3366-3374, 3378-3411, 3414-3419, 3423, 3434, 3438, 3441-3446, 3512, 3599-3607, 3612-3641, 3644-3653, 3657, 3661-3666, 3669-3673, 3746-3766, 3770-3811, 3816, 3824-3831, 3841-3849, 3859-3861, 3865-3869, 3873-3878, 3882-3898, 3901-3905, 3909, 3913, 3916-3920, 3980, 3986, 3991, 4035, 4222-4231, 4234-4236, 4239-4252, 4255-4269, 4273-4287, 4291-4311, 4316-4333, 4338-4363, 4367, 4497-4504, 4521-4522, 4526-4529, 4669-4677, 4697-4718, 4725-4733, 4741, 4746-4748, 4754, 4757-4759, 4765, 4768-4770, 4776, 4779-4781, 4787, 4791-4794, 4800, 4803-4805, 4811, 4815-4818, 4892-4906, 4910-4922, 4926, 4972-4992, 5088-5092, 5096-5103, 5108, 5176, 5182-5188, 5193 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pillike.py 343 243 29% 80-90, 122, 151, 182, 211, 244-249, 294-313, 320-347, 352-362, 415-426, 432, 446-531, 536-554, 597, 641, 684, 727, 732-750, 787, 824, 861, 898, 936, 973, 1010, 1047, 1084, 1121, 1132-1189, 1278-1316, 1373, 1444, 1451-1454, 1459, 1533-1540, 1558-1562, 1568-1574, 1578, 1583, 1643, 1709, 1775, 1841, 1855-1858, 1862-1865, 1870, 1917, 1967, 2018, 2070, 2122, 2173, 2224, 2274, 2324, 2375, 2489-2503, 2507-2513, 2520-2544, 2549-2567, 2572 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/pooling.py 113 76 33% 26-36, 49-59, 66-76, 83-92, 96-111, 115, 120, 126-150, 154-168, 172-174, 179-181, 186-188, 193, 312, 318, 433, 441, 556, 564, 679, 687 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/segmentation.py 353 269 24% 56-66, 212-222, 226-283, 287-319, 323, 357-378, 383-388, 392-393, 398-416, 421-441, 598-611, 615-634, 637-654, 658, 776, 940, 1122, 1174-1187, 1245-1248, 1253-1257, 1260-1263, 1267-1279, 1283-1286, 1290-1313, 1316, 1319, 1377-1381, 1387-1391, 1396-1410, 1413, 1417, 1463-1468, 1474-1495, 1498-1505, 1508-1512, 1515-1518, 1522-1538, 1541, 1545, 1580, 1585-1596, 1600-1603, 1608-1620, 1623, 1626, 1664-1671, 1676-1682, 1688-1692, 1695, 1699 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/size.py 1205 1047 13% 48-66, 70-72, 77-90, 95, 102, 110-147, 154-166, 170-174, 179-219, 225-253, 257, 262-279, 285-330, 348-350, 425-553, 607-621, 680-695, 734-766, 807-838, 874-902, 937-965, 1007-1037, 1084-1115, 1123, 1267-1272, 1276-1359, 1363-1378, 1382-1410, 1414-1434, 1438-1461, 1465-1475, 1478-1490, 1494-1529, 1533, 1539-1548, 1553, 1558, 1804-1828, 1833-1847, 1852-1902, 1906-1964, 1968-1997, 2001-2019, 2024-2043, 2047-2056, 2059-2169, 2173, 2377-2399, 2563-2585, 2731-2756, 2764-2790, 2794-2812, 2816-2831, 2836-2860, 2863-2884, 2889-2904, 2908, 2972, 3109-3121, 3129-3153, 3157-3171, 3175-3189, 3193-3204, 3210-3223, 3226-3245, 3249, 3310, 3381-3386, 3390-3412, 3417, 3477, 3551-3557, 3561-3583, 3588, 3657, 3739-3744, 3748-3770, 3775, 3835, 3914-3920, 3924-3946, 3951, 4019, 4085-4089, 4093-4118, 4123, 4180, 4248-4253, 4257-4279, 4284, 4340, 4399, 4461, 4523, 4579, 4701-4737, 4742-4781, 4787-4807, 4813-4825, 4830-4838, 4843-4855, 4858-4901, 4904-4908, 4912, 4916, 4919-4927 /home/admin/.local/lib/python3.8/site-packages/imgaug/augmenters/weather.py 226 166 27% 140-150, 153-159, 163-186, 190, 353-366, 371-379, 383, 394-414, 422-451, 457-466, 471-478, 484-495, 563-594, 666, 855-876, 880-888, 892, 903-953, 957-958, 965-969, 975, 979-988, 997-1004, 1012-1017, 1022-1023, 1028-1029, 1204-1217, 1301, 1311, 1317-1319, 1328-1339, 1428-1441 /home/admin/.local/lib/python3.8/site-packages/imgaug/dtypes.py 148 109 26% 20, 35-49, 54-97, 104, 108-112, 116-135, 150-172, 177-183, 187-189, 196-197, 201-207, 217-253, 258-282, 319-345 /home/admin/.local/lib/python3.8/site-packages/imgaug/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/imgaug/external/opensimplex.py 1412 1385 2% 14-15, 85, 100-113, 116-120, 123-129, 132-140, 148-244, 252-740, 748-1934 /home/admin/.local/lib/python3.8/site-packages/imgaug/imgaug.py 599 461 23% 14-15, 86-87, 106, 147, 165-187, 192, 365, 382, 400-402, 419, 440-448, 462, 491-506, 525-526, 541-542, 567-572, 586-587, 610-613, 632-633, 656-657, 670-671, 703-726, 761-802, 839-846, 866, 889-906, 928-957, 983-1003, 1029-1056, 1083-1106, 1142-1146, 1200-1216, 1273-1306, 1403-1572, 1576-1578, 1608-1619, 1691-1744, 1791, 1838, 1882, 1926, 1976-2034, 2061-2062, 2095-2123, 2142-2143, 2150-2151, 2153, 2180, 2227-2273, 2352-2355, 2369-2371, 2389-2391, 2402-2404, 2415-2417, 2455-2461 /home/admin/.local/lib/python3.8/site-packages/imgaug/parameters.py 1056 827 22% 37, 40-43, 51-62, 74-99, 112-153, 161-187, 197-247, 254-293, 299-301, 306-314, 320-336, 342, 387, 420, 423-425, 431-433, 439-441, 447-453, 459-461, 467-469, 475-477, 483-485, 491-493, 499-501, 507-513, 519-521, 527-529, 535-537, 551, 562, 594-628, 659, 663, 675, 678-682, 706-727, 731-739, 743, 747-750, 787-799, 802-856, 859, 862, 899-900, 903-907, 910, 913, 955-958, 962-968, 971, 974, 1014-1016, 1019-1022, 1025, 1028, 1068-1071, 1075-1080, 1083, 1086, 1143-1149, 1153-1170, 1173, 1176, 1220-1223, 1227-1232, 1235, 1238, 1273-1275, 1279-1281, 1284, 1287, 1323-1325, 1329-1331, 1334, 1337, 1379-1382, 1386-1392, 1395, 1398, 1438-1445, 1448-1452, 1455, 1458, 1538-1589, 1592-1653, 1656, 1659-1670, 1703-1715, 1718-1723, 1726, 1729-1737, 1766-1769, 1772-1791, 1794, 1797-1798, 1846-1850, 1853-1867, 1870, 1873, 1926-1930, 1934-1958, 1961, 1964, 2018-2022, 2025-2038, 2041, 2044, 2093-2097, 2100-2113, 2116, 2119, 2169-2173, 2176-2202, 2205, 2208, 2229-2233, 2236-2237, 2240, 2243-2244, 2269-2280, 2283-2300, 2303, 2306-2307, 2347-2364, 2367-2404, 2407, 2410-2411, 2449, 2491, 2570-2622, 2628-2659, 2662, 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/home/admin/.local/lib/python3.8/site-packages/importlib_resources/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_common.py 101 56 45% 35-46, 56, 68-72, 77, 82, 87, 95-104, 112-114, 129-141, 145, 156-158, 167, 176, 184-185, 194-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/_compat.py 58 36 38% 13, 20-23, 28-29, 42, 46, 49-75, 101-103, 107, 117-126 /home/admin/.local/lib/python3.8/site-packages/importlib_resources/abc.py 65 23 65% 26, 39, 47, 52, 79-80, 86-87, 109-124, 130, 161, 164, 167, 170 /home/admin/.local/lib/python3.8/site-packages/jmespath/__init__.py 12 4 67% 10-12, 19, 23 /home/admin/.local/lib/python3.8/site-packages/jmespath/ast.py 44 22 50% 6, 10, 14, 18, 22, 26, 30, 34, 38, 42, 46, 50, 54, 58, 62, 66, 70, 74, 78, 82, 86, 90 /home/admin/.local/lib/python3.8/site-packages/jmespath/compat.py 40 21 48% 16-48 /home/admin/.local/lib/python3.8/site-packages/jmespath/exceptions.py 68 36 47% 13-19, 23-24, 34-37, 41-42, 50-57, 60-61, 68-71, 74, 82-85, 91, 103-106, 109, 117 /home/admin/.local/lib/python3.8/site-packages/jmespath/functions.py 228 141 38% 73-81, 84-91, 94-97, 104-120, 124-134, 137-161, 166, 170-173, 177-179, 183-186, 190-193, 198-211, 215, 219, 223, 227, 231-234, 238, 242, 246, 250-253, 257-260, 264-267, 271-274, 278, 282, 288, 292, 296-307, 311-327, 331-337, 341-347, 350-359, 362 /home/admin/.local/lib/python3.8/site-packages/jmespath/lexer.py 139 120 14% 27-110, 114-118, 121-127, 130-135, 140-156, 159-176, 180-188, 193-196, 200-207 /home/admin/.local/lib/python3.8/site-packages/jmespath/parser.py 313 244 22% 79-82, 85-92, 95-105, 108-116, 119-135, 138, 141, 144-152, 155-160, 163, 166, 169-171, 174-177, 180-181, 184-198, 205-213, 219-237, 240, 243-244, 247-259, 262-263, 266-267, 270-271, 274-291, 295-301, 304, 307, 310, 313, 316, 319, 322-325, 328-344, 347-353, 356-357, 360-369, 372-389, 393-406, 417-434, 437-440, 443, 447-451, 455-458, 461, 464, 467, 470, 473-476, 480-488, 492-493, 498, 504-505, 508-510, 522-524, 527 /home/admin/.local/lib/python3.8/site-packages/jmespath/visitor.py 212 161 24% 9-12, 32-35, 43, 54-56, 70-71, 76-77, 80, 85, 88-94, 97, 113-123, 126, 129-132, 135-138, 142-158, 161, 164, 167-171, 174-184, 187-197, 200, 205-210, 213-216, 219-222, 225, 228, 231-236, 239-244, 247-250, 253-256, 259-264, 267-270, 273-281, 284-294, 300, 304, 309-311, 314-319, 322-328 /home/admin/.local/lib/python3.8/site-packages/lxml/__init__.py 11 9 18% 12-22 /home/admin/.local/lib/python3.8/site-packages/matplotlib/__init__.py 517 265 49% 165-178, 190-191, 223, 240-243, 276-277, 356-460, 465-480, 505, 511, 514-515, 521-537, 608-609, 617, 702-706, 708-709, 711-714, 717-718, 721-722, 724-725, 731-734, 737-740, 745-748, 758-764, 767, 775, 788-789, 803, 808-814, 821-828, 863-865, 872, 875-878, 889-902, 927-945, 977, 1033-1052, 1074-1077, 1090-1092, 1115-1119, 1168-1177, 1233-1240, 1252, 1263, 1270, 1288-1293, 1308-1316, 1320-1325, 1348, 1366, 1368, 1448-1472 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_afm.py 242 190 21% 54, 61-65, 69, 73-74, 78, 82-85, 105-168, 206-237, 252-269, 306-323, 339-355, 362-364, 367-369, 376-394, 398-424, 428, 432-434, 440-442, 446, 450-452, 458-459, 466, 470, 474, 478-481, 485-493, 498, 502, 506, 510, 514, 518, 525, 532 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/__init__.py 126 30 76% 47, 58, 83, 89-93, 124, 128-131, 158-168, 187, 191-192, 256, 270, 281, 336, 341, 357-359, 382 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_api/deprecation.py 173 33 81% 28-29, 142-143, 156-159, 162-164, 167-169, 292-296, 310, 370-373, 387, 392, 400-403, 449, 486-503 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_blocking_input.py 8 7 12% 21-30 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm.py 141 12 91% 59-64, 145-152 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_cm_listed.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_color_data.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_constrained_layout.py 373 352 6% 102-149, 162-194, 202-240, 247-260, 264-297, 303-335, 347-440, 447-479, 507-576, 583-596, 615-624, 632-665, 689-751, 761-768, 772-783 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_docstring.py 39 4 90% 35, 53, 59-60 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_enums.py 57 36 37% 24, 89-111, 161-177 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_fontconfig_pattern.py 46 7 85% 89-91, 97, 101-105, 114-118 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_layoutgrid.py 208 174 16% 40-103, 106-118, 126-128, 132-137, 144-162, 166, 173-206, 213-245, 266-267, 287-288, 303-304, 322-323, 339-347, 352, 359-367, 374-391, 398-411, 418-429, 436-448, 455-466, 473-484, 490, 497, 502-547 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext.py 1244 988 21% 57-66, 100-102, 105-111, 116-147, 170-171, 180, 218-219, 227-228, 234, 240, 247, 255, 264, 274-282, 285-295, 298-300, 304-323, 334-343, 349, 353-358, 380-387, 392-405, 464, 489-519, 524, 527-586, 589-592, 599-617, 621-632, 699-704, 709-753, 757-773, 912-919, 926, 929, 932, 939, 949-952, 955-959, 962, 969, 976, 993-1002, 1005, 1008-1015, 1018, 1027-1034, 1037, 1042-1047, 1057-1061, 1064-1065, 1068, 1077-1083, 1086, 1093-1104, 1108-1113, 1120-1123, 1133-1148, 1187-1226, 1233-1234, 1258-1305, 1320-1321, 1324, 1331-1334, 1341-1342, 1368-1375, 1378-1381, 1391, 1401, 1419-1420, 1423, 1426-1428, 1441-1467, 1480-1495, 1508-1637, 1646-1649, 1664-1668, 1671, 1675, 1679-1681, 1685, 1701-1711, 1800-1955, 1964-1977, 1981, 1985, 1989, 1992, 1995, 1998-2000, 2003-2009, 2019-2028, 2046-2048, 2051, 2054-2096, 2099, 2127-2141, 2148-2150, 2153-2185, 2188-2192, 2195-2196, 2199, 2204-2205, 2208-2209, 2212-2216, 2219-2221, 2224-2226, 2229, 2232-2391, 2394-2429, 2432, 2435, 2441, 2446, 2451, 2456-2483, 2488-2525, 2528-2544, 2547-2566, 2569 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_mathtext_data.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/_pylab_helpers.py 67 27 60% 41, 55-67, 72-75, 80-83, 88, 93, 98, 115, 130-132 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_text_helpers.py 23 1 96% 34 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_bbox.py 47 44 6% 18-70, 80-84 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_tight_layout.py 133 125 6% 48-157, 170-191, 226-301 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_type1font.py 396 320 19% 56-58, 61, 65, 69, 73, 77, 81, 84, 91, 94, 101, 108, 115, 118, 137-141, 145-151, 158, 165, 168-171, 190-270, 294-315, 364-373, 377-402, 415-441, 457-463, 482-489, 498-592, 595-626, 630-653, 660-680, 684-692, 714-770 /home/admin/.local/lib/python3.8/site-packages/matplotlib/_version.py 11 2 82% 5-6 /home/admin/.local/lib/python3.8/site-packages/matplotlib/artist.py 664 260 61% 36-37, 58-61, 66-67, 70, 75, 77, 81-82, 97-98, 113, 145, 217-221, 241-259, 304, 317, 327, 350, 367-376, 405, 415, 457, 462, 483-485, 504-508, 518, 531-555, 590, 602, 616, 630, 641, 713-717, 727-728, 754, 813-814, 816, 822-823, 825-826, 828-829, 832, 864, 879-881, 892, 896, 908-910, 959-964, 985-986, 1003-1005, 1017, 1020, 1036, 1040-1046, 1076-1078, 1127, 1178, 1193, 1197, 1271-1286, 1317, 1337-1373, 1397-1403, 1414-1417, 1435-1437, 1441, 1485, 1493, 1515-1519, 1596-1600, 1614, 1616-1617, 1635-1666, 1683-1700, 1704-1715, 1746-1751, 1816-1838 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/__init__.py 9 1 89% 10 /home/admin/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py 2254 1885 16% 98-102, 158, 172, 179, 193-195, 317, 323, 381-398, 461-511, 547-550, 585-591, 617-623, 704, 761-776, 829-844, 849-851, 904-926, 966-974, 1022-1031, 1072-1111, 1152-1191, 1304-1437, 1772-1776, 1821-1829, 1872-1876, 1919-1923, 1996, 2073-2105, 2174-2176, 2190-2228, 2340-2525, 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78-84, 91-97, 102-121, 126-129, 136-137, 144-145, 153-154, 162-168, 185-189, 205-225 /home/admin/.local/lib/python3.8/site-packages/matplotlib/image.py 760 617 19% 83-110, 134-157, 171-213, 221-227, 259-274, 277-281, 285, 289-292, 302-306, 318, 325-326, 358-587, 607, 615, 620-646, 650-677, 681-683, 695-731, 743, 754, 771-776, 788-792, 796-797, 811-814, 818, 830-831, 835, 846-850, 854, 920-922, 936-938, 942-949, 954, 977-1002, 1006-1014, 1025-1041, 1058-1059, 1063, 1067-1133, 1148-1165, 1168, 1177-1180, 1183-1185, 1188, 1191, 1194-1196, 1199-1201, 1245-1248, 1252-1281, 1284, 1304-1338, 1341, 1345-1354, 1379-1389, 1393-1394, 1399-1410, 1416-1417, 1440-1451, 1454-1462, 1466-1476, 1480-1486, 1538, 1541-1553, 1558, 1621, 1627-1633, 1641, 1648, 1657, 1665-1666, 1679-1686, 1711-1724, 1734-1754, 1796-1818 /home/admin/.local/lib/python3.8/site-packages/matplotlib/layout_engine.py 69 39 43% 63-64, 70, 78-80, 88-90, 96, 103, 122-124, 130, 158-162, 181-189, 207-209, 249-259, 269-274, 303-305 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67 41% 55-57, 61-63, 70, 76, 83, 90, 100-105, 108, 114-116, 119-125, 130-139, 142-144, 147-148, 161-163, 166-167, 170, 173, 225-226, 230-252, 278-287 /home/admin/.local/lib/python3.8/site-packages/matplotlib/mlab.py 275 235 15% 69, 80, 108-127, 152-157, 179, 198-213, 246-250, 255-288, 298-446, 455-472, 584-587, 638-651, 772-790, 829-840, 888-925, 929, 932, 959-985 /home/admin/.local/lib/python3.8/site-packages/matplotlib/offsetbox.py 659 353 46% 66-67, 73, 131-154, 196-197, 199-200, 271-278, 325-326, 336-337, 362, 387-388, 393-394, 399, 403-404, 481-483, 514, 552-564, 568-569, 573-583, 586-589, 593-594, 631, 635-636, 665, 678, 680, 683, 702, 748-749, 753, 764-765, 771, 794, 807-809, 841-846, 850-852, 859, 877-880, 884, 888-898, 902-905, 971-990, 1001-1004, 1008, 1012, 1016-1018, 1022-1029, 1040-1054, 1059-1065, 1068-1070, 1074-1086, 1131-1140, 1161-1178, 1181-1183, 1186, 1189-1190, 1193, 1197, 1200, 1203-1208, 1212-1214, 1229, 1311-1341, 1345, 1349-1350, 1354, 1358-1359, 1362-1367, 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397, 400-403, 406, 421-423, 427-442, 446, 454-456, 460-473, 477, 483-488, 492-493, 496, 502 /home/admin/.local/lib/python3.8/site-packages/matplotlib/projections/polar.py 719 577 20% 50-54, 63, 68-77, 81-131, 135, 165-169, 175-184, 207-210, 219-231, 235, 246-253, 259, 262, 265, 268, 271, 274, 277, 290-291, 294-295, 298-302, 305-306, 324-332, 337-344, 347-352, 355-396, 413-416, 420-422, 425-433, 437-445, 458-459, 462, 466-471, 478-479, 483-487, 490-494, 514-518, 523-544, 559-561, 566-615, 618-695, 710-711, 714-716, 720-722, 725-726, 735, 744, 761-765, 771-800, 815-821, 825-844, 848-849, 857-951, 955-956, 959, 962, 965-970, 973-982, 985-991, 994-1037, 1040, 1043-1053, 1057, 1061, 1065, 1069, 1087-1098, 1104-1106, 1112, 1129-1138, 1150-1159, 1171, 1181, 1190, 1200, 1209, 1219, 1227, 1230, 1244-1256, 1266, 1277, 1280-1281, 1285, 1288, 1340-1349, 1402-1415, 1419-1441, 1453, 1463, 1473, 1476-1486, 1496, 1499-1523 /home/admin/.local/lib/python3.8/site-packages/matplotlib/pyplot.py 860 424 51% 119-120, 136, 145-157, 163-167, 175, 180-182, 188-191, 208, 234-267, 276-279, 304-322, 337, 352, 362-373, 375, 445-446, 476, 512-516, 552-556, 576-584, 589, 594, 599-601, 609, 614, 619, 658-686, 804-807, 814-825, 831, 846, 849-853, 867, 889-890, 906, 911, 921-923, 940, 945, 950, 969-991, 1017, 1022-1025, 1032, 1118-1126, 1133-1135, 1142-1143, 1149, 1290-1352, 1613-1621, 1664-1683, 1696-1699, 1712-1715, 1726-1734, 1753-1756, 1791-1795, 1830, 1881-1883, 1893, 1944-1946, 1954-1956, 2020-2029, 2088-2097, 2105-2108, 2116-2118, 2130-2138, 2155-2159, 2165, 2184-2190, 2195, 2242-2252, 2267-2274, 2284, 2295, 2303, 2315, 2324, 2335, 2343, 2349, 2355, 2361, 2370, 2380, 2389, 2395, 2401, 2407, 2419, 2425, 2431, 2439, 2447, 2457, 2467, 2483, 2500, 2508, 2517, 2527-2531, 2537-2541, 2550, 2564, 2578, 2588, 2598, 2608, 2627-2635, 2645, 2658, 2669-2674, 2682, 2695-2704, 2716, 2722, 2730, 2739, 2745, 2751, 2759-2764, 2773-2778, 2786, 2822, 2833, 2843-2847, 2853, 2874, 2880, 2890-2897, 2905-2909, 2917, 2929, 2940, 2953-2963, 2991, 2999, 3008-3010, 3016-3018, 3026-3030, 3036, 3045, 3058, 3069, 3107, 3113, 3124, 3135, 3146, 3157, 3168, 3179, 3190, 3201, 3212, 3223, 3234, 3245, 3256, 3267, 3278, 3289, 3300, 3311, 3322 /home/admin/.local/lib/python3.8/site-packages/matplotlib/quiver.py 390 338 13% 291-314, 318, 322-345, 348, 357-362, 365, 373-374, 377-385, 407-437, 441-443, 477-506, 516-527, 530-536, 540-544, 549-571, 575-577, 592-596, 599-605, 608-663, 670-723, 897-941, 967-973, 1024-1117, 1122-1162, 1173-1180 /home/admin/.local/lib/python3.8/site-packages/matplotlib/rcsetup.py 414 127 69% 68-69, 75-82, 99, 110, 127, 135-136, 159, 169-170, 185, 188-189, 218, 230-234, 238, 260, 282, 288, 290, 292, 294, 296-300, 304, 344-347, 354, 366-367, 381-384, 395-398, 411, 415, 427-428, 438, 457-483, 506-524, 534, 537-541, 549-552, 560, 568, 583-589, 683, 686, 689-692, 695-698, 705, 716-718, 738-739, 746, 750, 758, 761, 783, 786-792 /home/admin/.local/lib/python3.8/site-packages/matplotlib/scale.py 274 146 47% 69, 76, 111, 145-150, 153, 156, 180-182, 186, 190-198, 205-209, 213, 218-236, 239, 246-247, 250, 253, 256, 281-282, 288-291, 297, 301-304, 333-335, 339, 343, 350-361, 364-371, 374, 382-388, 391-398, 401, 440-441, 449-453, 457, 465-469, 472, 475, 483-484, 487, 490, 551-557, 562, 565-574, 581-584, 588-593, 596, 599, 606-607, 611, 614, 617, 646-648, 652, 657-665, 677-679, 726 /home/admin/.local/lib/python3.8/site-packages/matplotlib/spines.py 315 142 55% 33, 90-99, 103-109, 113-114, 126-131, 137-138, 156, 171-176, 189-194, 231, 234, 240, 243-270, 282, 314, 317, 319, 329-330, 338-341, 351, 357-386, 408-419, 423, 438, 448-451, 456-460, 476-477, 491, 494-505, 508-512, 546, 549, 554-555, 560-563, 566, 568-571, 578, 582 /home/admin/.local/lib/python3.8/site-packages/matplotlib/stackplot.py 42 37 12% 71-127 /home/admin/.local/lib/python3.8/site-packages/matplotlib/streamplot.py 370 328 11% 91-241, 247-248, 274-284, 288, 291, 294, 297, 300-301, 304-305, 308-311, 314, 321-362, 366, 372, 386-396, 399, 403-404, 408-409, 417-426, 443-502, 535-602, 607-624, 633-667, 678-707 /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/matplotlib/style/core.py 92 45 51% 22, 127-180, 220-224, 242, 256, 262-266 /home/admin/.local/lib/python3.8/site-packages/matplotlib/table.py 335 92 73% 101, 118, 143, 161, 188-189, 202, 207, 211-217, 302, 354-355, 365, 402, 404, 407, 431-444, 448, 452-457, 500-508, 512-516, 520-521, 526, 532, 543-545, 568-570, 585, 598-605, 614, 616, 618, 620, 627, 629, 631, 633, 650, 738, 744-746, 752, 757, 760, 774-777, 780-781, 787-790, 796, 821-827 /home/admin/.local/lib/python3.8/site-packages/matplotlib/texmanager.py 151 103 32% 48-49, 105-106, 110-115, 120-130, 134-171, 178-187, 194-195, 200, 205-207, 246-249, 253-275, 284-305, 314-329, 334-344, 357-361, 366-373 /home/admin/.local/lib/python3.8/site-packages/matplotlib/text.py 812 357 56% 41-49, 67-90, 130, 228, 233, 236-239, 246-268, 279, 292-313, 318, 390, 479, 486-489, 492, 494, 496, 498, 531-552, 559, 571-582, 589, 633-652, 659-675, 681-685, 697-736, 761-762, 768-769, 785, 789-790, 796, 814, 824, 834, 844, 864, 874, 884, 916, 941, 952, 954, 977-983, 1026-1028, 1065-1066, 1080-1081, 1095-1096, 1126, 1148, 1181-1182, 1234, 1246-1247, 1297-1299, 1301, 1303, 1332, 1371, 1395-1397, 1407-1408, 1412, 1415-1419, 1436-1454, 1470-1478, 1483-1488, 1490-1496, 1498-1499, 1501, 1503, 1505, 1508, 1510-1513, 1517, 1524, 1526, 1537-1538, 1545-1546, 1548, 1550-1552, 1557, 1562, 1569, 1606-1607, 1612, 1616-1617, 1639-1654, 1673, 1856, 1872-1880, 1888-1895, 1908, 1918, 1922, 1938, 1946, 1960-2016, 2024, 2030-2032, 2041-2058, 2062-2064 /home/admin/.local/lib/python3.8/site-packages/matplotlib/textpath.py 192 152 21% 34-37, 40, 46, 49-70, 112-134, 142-164, 173-215, 221-223, 230-280, 287-298, 354-369, 373-374, 378, 385-386, 393, 402-408 /home/admin/.local/lib/python3.8/site-packages/matplotlib/ticker.py 1228 803 35% 165-167, 170, 173, 176, 179, 182, 186, 196-197, 213, 225, 233, 269, 283-284, 294-297, 300, 303, 316-317, 325, 328, 331, 346, 354, 365, 374, 452, 485-486, 498, 512, 531, 555-556, 559-564, 573, 588, 620-622, 626-650, 654-666, 676-692, 712-713, 736, 749-750, 753-754, 761-762, 764, 768, 772, 774, 782, 789, 792, 795, 805, 809, 874-883, 893, 902, 914, 925, 933-984, 987-993, 997-1016, 1019-1020, 1024, 1028-1046, 1054-1062, 1072, 1076-1110, 1120-1125, 1167-1172, 1184, 1193, 1205, 1218, 1221-1260, 1263-1286, 1289-1292, 1295-1313, 1318-1322, 1388-1392, 1395, 1398-1401, 1406, 1409-1412, 1417-1421, 1438-1473, 1503-1506, 1510-1512, 1536-1556, 1559, 1570-1579, 1583, 1616, 1623, 1631, 1645, 1685-1686, 1690-1693, 1697-1698, 1701, 1723-1724, 1741-1747, 1791-1795, 1800, 1804, 1808-1811, 1815-1816, 1819-1830, 1835-1850, 1860, 1864-1865, 1869-1870, 1873-1879, 1886-1896, 1905, 1928, 1935-1937, 1946, 1953, 2013, 2017, 2020, 2022, 2053, 2065, 2072, 2086-2088, 2097-2098, 2104-2109, 2117, 2140-2141, 2148, 2150, 2152, 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/home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trifinder.py 26 15 42% 20-21, 38-42, 55-63, 79, 86, 93 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triinterpolate.py 535 450 16% 34-56, 157-207, 228, 258-261, 265, 270, 275-283, 381-418, 421, 426, 431-446, 466-476, 497-515, 539-543, 561-571, 689-706, 727-762, 783-787, 803-828, 846-879, 896-909, 935-978, 996-1004, 1007, 1013-1017, 1043-1058, 1065-1068, 1084-1105, 1112-1127, 1135-1153, 1163-1164, 1172-1210, 1224-1227, 1234-1235, 1243-1248, 1254-1259, 1265-1269, 1272, 1277-1280, 1312-1350, 1406-1426, 1440-1472, 1479, 1486, 1494-1514, 1531-1544, 1556-1574 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_tripcolor.py 62 56 10% 61-154 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_triplot.py 28 23 18% 38-86 /home/admin/.local/lib/python3.8/site-packages/matplotlib/tri/_trirefine.py 93 81 13% 43-44, 62, 94-131, 157-169, 191-307 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/home/admin/.local/lib/python3.8/site-packages/numpy/lib/_datasource.py 177 73 59% 59-66, 110-111, 116-117, 123-126, 252-254, 259-261, 274-278, 289, 325-342, 364-366, 371, 373, 411, 468-485, 522, 529, 533, 578-579, 582, 586-591, 595, 618, 652, 683, 700-704 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_iotools.py 352 295 16% 31-33, 44-45, 53-57, 81-84, 120-131, 168, 172-197, 201-206, 209-216, 219-224, 227, 288-310, 339-380, 383, 413-419, 505, 524, 529, 539-541, 568-582, 587-596, 601-669, 672-675, 678-700, 703, 707-723, 746-751, 754-763, 796-820, 861-898 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/_version.py 75 61 19% 56-76, 80-97, 101-112, 115-134, 137, 140, 143, 146, 149, 152, 155 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraypad.py 218 200 8% 29-30, 55, 81-83, 109-126, 146-151, 175-183, 208-227, 257-293, 321-378, 401-451, 482-518, 522, 736-876 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arraysetops.py 197 117 41% 34, 82-122, 130, 276-317, 330-331, 338-344, 350, 352-355, 357-358, 435-436, 441-442, 446-447, 455-461, 467, 500-510, 589, 600-602, 610-631, 738, 775, 779, 819-824 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/arrayterator.py 71 58 18% 85-90, 93, 101-125, 132-134, 161-162, 172, 177-219 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/format.py 266 178 33% 193-194, 212-216, 232-233, 238-245, 270-281, 307-336, 352-364, 371-389, 396-413, 430-440, 452, 468, 499, 532, 563, 579, 599-601, 603-604, 607-609, 614-615, 617-618, 621-623, 663-696, 735, 742-751, 770-780, 784-785, 842-890, 912-913, 915-916 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/function_base.py 1153 803 30% 116, 121, 125, 131, 133, 143, 232, 234, 383-412, 415-417, 488, 494-497, 588-618, 622-623, 665-719, 810-811, 989-1157, 1250, 1252, 1258, 1263-1268, 1273-1278, 1281, 1298, 1406-1439, 1443, 1485-1496, 1500, 1569-1596, 1600, 1627-1637, 1686, 1689-1693, 1698, 1750, 1754, 1794-1798, 1833-1840, 1866-1869, 1887-1905, 1926-1934, 1939, 1945-1948, 2113, 2118, 2122, 2131, 2148-2161, 2169, 2186, 2197, 2208-2214, 2219, 2237, 2239, 2251, 2257-2316, 2321, 2448-2543, 2548, 2679-2701, 2796-2801, 2905-2910, 3009-3014, 3109-3114, 3182-3190, 3194, 3198, 3202, 3256-3262, 3388-3392, 3396, 3475-3477, 3481, 3508-3510, 3542-3560, 3570, 3655-3660, 3666-3716, 3866, 3873, 3976-3979, 3990, 3998-4000, 4003, 4030-4033, 4038, 4047, 4053, 4055, 4057, 4059, 4063, 4075-4084, 4095-4098, 4116-4120, 4126, 4215-4240, 4356, 4379, 4447-4560, 4564, 4656-4755, 4814, 4821, 4915-4932 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/histograms.py 287 254 11% 29, 49-50, 72-73, 96-97, 118-119, 146-161, 182-196, 224-226, 263-270, 285-301, 309-331, 342-357, 382-451, 460, 467, 668-670, 675, 791-929, 934-940, 1014-1129 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/index_tricks.py 258 182 29% 32, 93-107, 149-207, 325-420, 423, 593, 607, 610, 657-661, 665, 678-681, 695-696, 775, 891-909, 977-978, 982, 1006-1013 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/mixins.py 59 12 80% 10-13, 19-21, 29-31, 39, 54 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/nanfunctions.py 279 219 22% 61-66, 96-110, 135-139, 164-180, 209-221, 225, 313-336, 340, 428-451, 455, 494-500, 504, 544-550, 554, 647-648, 652, 717-718, 722, 787-788, 792, 854-855, 859, 937-957, 965-974, 984-1000, 1010-1020, 1025, 1113-1124, 1129, 1245-1249, 1255, 1358-1362, 1371-1381, 1391-1405, 1413-1418, 1424, 1518-1567, 1572, 1670-1676 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/npyio.py 854 664 22% 34-37, 83, 86-89, 97, 108-112, 189-202, 205, 208, 215-221, 224, 228, 231, 242-260, 269-273, 277-281, 408, 414-415, 431-434, 438, 444-450, 455, 519-529, 534-535, 618, 622-623, 689, 695-726, 733-736, 740, 742, 744, 747-758, 768, 904, 927-944, 950, 954, 990, 992, 994-995, 1005-1006, 1016, 1028, 1039-1054, 1065, 1072-1075, 1081, 1085-1086, 1091, 1100-1104, 1113, 1122-1138, 1152-1156, 1162, 1167, 1176-1179, 1182-1186, 1199, 1326-1447, 1510-1541, 1557, 1751-2284, 2311-2317, 2339-2345, 2369-2377, 2403-2415 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/polynomial.py 438 319 27% 41, 138-165, 231, 238, 248, 257, 265, 342-366, 370, 432-446, 450, 620-689, 693, 764-772, 776, 830-844, 884-898, 956-961, 965, 1020-1038, 1042-1065, 1180, 1185-1186, 1191, 1197, 1202, 1208, 1220-1231, 1233, 1236, 1239, 1246-1249, 1252-1254, 1257, 1260-1314, 1317, 1320, 1323, 1326-1330, 1333-1337, 1340-1341, 1344-1345, 1348-1353, 1356-1357, 1360-1361, 1364-1368, 1373-1377, 1382-1386, 1389-1391, 1395-1400, 1403-1411, 1414, 1427, 1440 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/scimath.py 70 38 46% 106-110, 135-138, 162-165, 189-192, 196, 239-240, 287-288, 337-338, 342, 376-378, 425-426, 430, 473-475, 519-520, 565-566, 616-617 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/shape_base.py 263 148 44% 31-49, 53, 161-170, 174, 251-260, 264, 359-414, 418, 487-505, 509, 588-602, 648, 718, 722, 727-732, 778, 868-872, 878, 937-942, 989-991, 1034-1036, 1043-1048, 1055-1060, 1064, 1136-1164, 1252 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py 91 46 49% 21-22, 29-34, 97-114, 119, 301-335, 343, 345, 363, 411, 426-427, 470-471 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/twodim_base.py 177 100 44% 34-40, 95-98, 153, 158, 211, 216, 220, 295, 300-303, 346-363, 367, 412-424, 433, 469-472, 498-501, 505, 582-597, 602-615, 741-752, 821-823, 904-906, 911, 938-940, 1023-1025, 1057-1059 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/type_check.py 138 78 43% 70-77, 81, 112-114, 159-160, 202-203, 240-244, 339-340, 390, 395-397, 401, 498-521, 526, 574-583, 588-590, 618, 696, 713, 753-769 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/ufunclike.py 57 30 47% 24-36, 50-53, 65, 70, 117-124, 188-196, 260-268 /home/admin/.local/lib/python3.8/site-packages/numpy/lib/utils.py 454 363 20% 38-46, 83-84, 96-97, 116, 123-124, 221, 260-277, 329-379, 391-407, 415-431, 452-482, 537-628, 672-677, 737-812, 835-947, 950-956, 1028-1043, 1056-1070 /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py 678 507 25% 88, 91, 94, 97, 100, 129, 133, 142, 146-149, 153-154, 165-174, 177-185, 188-190, 196, 203, 208, 212, 230, 235, 286-306, 310, 378-395, 399, 456-467, 550, 618-666, 756-764, 770, 890-982, 1075, 1081, 1159-1176, 1179-1181, 1314-1333, 1453-1473, 1624-1639, 1652-1657, 1666-1674, 1678, 1761-1797, 1801, 1898-1906, 1912, 1995-2011, 2092-2101, 2153-2160, 2166, 2266-2328, 2354-2356, 2360, 2514-2611, 2617-2618, 2707-2739, 2750-2760, 2780-2801, 2806-2812 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/ma/core.py 2405 1672 30% 102-113, 122, 124, 204-212, 217-222, 270-277, 282-291, 342, 393, 414-425, 441, 443-451, 456-457, 463-468, 532-534, 543-547, 575-579, 628, 633-636, 646-663, 713, 768-776, 804, 812-813, 831-832, 848-853, 868-869, 884-885, 895, 928-969, 1011-1050, 1057-1080, 1087-1105, 1112-1116, 1155-1192, 1284-1291, 1295-1297, 1305, 1534-1537, 1547, 1621-1636, 1683, 1726-1753, 1788-1809, 1814-1817, 1924-1943, 1969, 1995, 2021, 2047, 2073, 2101-2103, 2139-2143, 2179-2183, 2244-2251, 2323-2331, 2370, 2400, 2407, 2414, 2421, 2424, 2439-2447, 2489-2495, 2525-2550, 2581-2590, 2647-2653, 2656, 2659-2670, 2674-2676, 2696-2703, 2834, 2840, 2845-2847, 2857-2861, 2869-2871, 2874-2875, 2879-2882, 2887, 2889, 2895-2896, 2900-2909, 2915-2932, 2938, 2943, 2956, 3012, 3033, 3037-3040, 3047, 3054-3058, 3062, 3065, 3074-3121, 3180-3183, 3190-3194, 3204-3210, 3236-3251, 3261-3267, 3276, 3283-3286, 3291, 3301-3330, 3347-3402, 3411-3419, 3427-3431, 3438-3500, 3516, 3532-3535, 3539, 3554-3555, 3570-3571, 3576, 3591-3594, 3599, 3635, 3660, 3664-3665, 3697-3706, 3710-3725, 3778, 3781, 3786, 3789-3790, 3794-3809, 3833-3836, 3898-3908, 3915-3939, 3942, 3949-4026, 4031-4040, 4053-4104, 4117, 4130, 4137-4139, 4148, 4155-4157, 4164, 4168-4170, 4179, 4186-4188, 4195-4197, 4204, 4211-4213, 4220, 4227-4229, 4236, 4243-4253, 4260-4269, 4276-4285, 4292-4303, 4310-4321, 4328-4339, 4346-4360, 4367-4373, 4380-4385, 4407-4409, 4434-4436, 4497-4538, 4588, 4652-4658, 4673-4676, 4739-4761, 4785-4787, 4815, 4841-4855, 4871-4885, 4983, 4990-4997, 5037, 5078-5099, 5133-5140, 5160-5181, 5206-5213, 5241-5260, 5293-5300, 5317-5363, 5380-5388, 5401-5413, 5479-5493, 5535-5538, 5572-5575, 5648-5658, 5692-5724, 5788-5792, 5826-5859, 5936-5947, 5950-5953, 5956-5959, 5964-5985, 6024-6046, 6057-6061, 6101, 6116, 6163-6175, 6184-6186, 6200-6203, 6209, 6214-6221, 6229-6231, 6241-6257, 6262, 6269-6284, 6287-6291, 6294-6302, 6308-6316, 6319, 6343, 6356-6366, 6462-6469, 6472, 6475, 6478, 6481-6485, 6491-6500, 6505, 6510, 6524, 6527, 6530, 6536-6542, 6559, 6614-6616, 6640-6647, 6651-6679, 6683-6695, 6698-6705, 6710-6717, 6723-6729, 6767-6768, 6775, 6812-6813, 6833-6865, 6872-6882, 6898-6908, 6923, 6967-6983, 6998-7001, 7016-7022, 7037-7043, 7059-7062, 7083-7101, 7136-7139, 7156-7158, 7216-7222, 7230, 7237, 7243, 7308-7340, 7388-7415, 7442-7448, 7527-7540, 7605-7625, 7636-7642, 7650-7660, 7670-7682, 7710, 7738, 7783-7796, 7871-7904, 8001, 8011, 8017, 8082, 8116-8127, 8186 /home/admin/.local/lib/python3.8/site-packages/numpy/ma/extras.py 560 464 17% 48, 101-102, 152-154, 207-209, 259, 262, 272-280, 306-318, 333-341, 364-369, 376-451, 459-477, 587-631, 700-714, 719-799, 823-842, 894-896, 911-914, 928-931, 975-981, 1024-1030, 1049-1063, 1078-1087, 1113-1119, 1133-1146, 1168-1188, 1209-1210, 1225, 1248-1253, 1267-1301, 1358-1374, 1425-1461, 1486-1487, 1491-1494, 1569-1576, 1621-1626, 1674-1684, 1744-1760, 1769-1789, 1825-1828, 1864-1867, 1880-1884, 1894-1921 /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py 238 158 34% 15-33, 69, 123-128, 131-139, 142, 149, 151, 153, 157, 160, 173-187, 190-213, 216-221, 224, 227-228, 231, 234-235, 238, 244-251, 257-260, 284, 319, 372, 411, 445, 479, 513, 546, 569, 609, 644, 683, 718, 757, 790, 830-835, 865, 894, 933, 966, 998-1001, 1011-1032, 1089-1111 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/__init__.py 18 7 61% 171-180 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/_polybase.py 419 296 29% 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 193-198, 216, 234, 252, 280-288, 291-304, 307-311, 314-324, 327-329, 337-367, 375-380, 389-394, 398-403, 409, 413-462, 469-473, 476, 481-483, 486, 489, 494, 497, 500-505, 508-513, 516-521, 527-532, 535-538, 541-544, 547-556, 559-561, 564-568, 571-575, 578-582, 586, 591, 594-597, 600-603, 606-614, 617-622, 625, 640, 653, 678, 700-701, 723-730, 761-767, 796, 823-829, 849-851, 865-866, 894-898, 971-986, 1014-1027, 1054-1060, 1091-1099, 1137-1141 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/chebyshev.py 357 294 18% 152-155, 177-180, 207, 243-274, 302-306, 333-340, 389-394, 441-455, 508-511, 566, 608, 652, 686-698, 742-747, 797-814, 855-872, 935-964, 1052-1091, 1153-1175, 1224, 1277, 1328, 1384, 1422-1437, 1490, 1544, 1670, 1700-1715, 1766-1776, 1827-1843, 1881-1888, 1915-1916, 1946-1953, 1979-1986, 2065-2069 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite.py 267 214 20% 134-139, 180-197, 251-254, 310, 350, 390, 432-443, 485-509, 557, 594, 652-677, 763-799, 871-895, 944, 997, 1048, 1104, 1151-1165, 1218, 1272, 1403, 1433-1448, 1502-1512, 1544-1555, 1594-1622, 1649-1650 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/hermite_e.py 264 211 20% 135-140, 181-197, 250-253, 309, 349, 389, 427-438, 480-504, 550, 587, 645-670, 756-792, 864-887, 936, 989, 1040, 1096, 1143-1156, 1209, 1263, 1395, 1426-1441, 1495-1505, 1537-1548, 1587-1615, 1641-1642 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/laguerre.py 252 200 21% 134-138, 179-193, 245-248, 304, 345, 385, 427-439, 481-505, 551, 588, 646-674, 761-798, 870-893, 942, 995, 1046, 1102, 1149-1162, 1215, 1269, 1400, 1429-1444, 1498-1508, 1547-1572, 1598-1599 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/legendre.py 261 209 20% 140-145, 193-207, 261-264, 319, 361, 405, 447-461, 505-529, 578, 609, 672-701, 789-829, 891-914, 963, 1016, 1067, 1123, 1161-1176, 1229, 1283, 1411, 1441-1455, 1506-1516, 1555-1584, 1611-1612 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polynomial.py 221 166 25% 145-148, 212, 248, 285, 317-325, 361-363, 400-421, 460, 515-542, 623-661, 745-757, 835-845, 895, 948, 999, 1055, 1096-1109, 1157, 1211, 1361, 1390-1401, 1454-1464, 1514, 1518, 1522-1529 /home/admin/.local/lib/python3.8/site-packages/numpy/polynomial/polyutils.py 229 204 11% 71-77, 130-153, 200-208, 248-254, 297-301, 366-368, 372-374, 422-443, 452-453, 469-483, 497-513, 527-529, 547-565, 571-578, 584-592, 606-680, 697-713, 732-750 /home/admin/.local/lib/python3.8/site-packages/numpy/random/__init__.py 17 1 94% 210 /home/admin/.local/lib/python3.8/site-packages/numpy/random/_pickle.py 22 12 45% 31-37, 54-60, 77-83 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/decorators.py 74 61 18% 61-65, 100-105, 143-186, 226-251, 282-304, 323-329 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/nosetester.py 174 157 10% 36-58, 96-109, 164-193, 212-230, 233-250, 259-260, 276-324, 397-463, 523-536, 540-544 /home/admin/.local/lib/python3.8/site-packages/numpy/testing/_private/utils.py 873 732 16% 59-75, 89-95, 109-113, 127-132, 146-151, 156-186, 196-208, 220-244, 249-272, 325-432, 463-473, 545-599, 660-698, 716, 741-745, 749, 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/home/admin/.local/lib/python3.8/site-packages/oauth2client/_pkce.py 14 8 43% 41-49, 66-67 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pure_python_crypt.py 63 39 38% 55-62, 73, 88-92, 113-125, 136, 147-148, 166-184 /home/admin/.local/lib/python3.8/site-packages/oauth2client/_pycrypto_crypt.py 38 22 42% 34, 48-49, 64-75, 87, 98-99, 116-124 /home/admin/.local/lib/python3.8/site-packages/oauth2client/client.py 708 520 27% 147-148, 184-187, 213, 222, 231, 239, 255-274, 283, 298-314, 328, 351, 358-359, 367-368, 378, 388, 395, 405-409, 419-423, 434-438, 489-506, 535-536, 545, 554, 562, 580-581, 595-596, 610-633, 641-652, 660-664, 677, 689-697, 701, 705-707, 711-712, 716-722, 726-733, 748-763, 774-819, 827, 841-863, 871, 885-902, 944, 956-960, 971, 980, 996-1005, 1014-1030, 1039-1045, 1101, 1111, 1118, 1124-1147, 1152, 1171-1174, 1187-1190, 1207-1230, 1249-1261, 1271, 1287-1298, 1310-1315, 1331-1340, 1344-1351, 1362-1379, 1385-1415, 1420, 1427, 1433-1435, 1439-1441, 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/home/admin/.local/lib/python3.8/site-packages/pandas/_libs/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/tslibs/__init__.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_libs/window/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/__init__.py 396 256 35% 121-125, 253-255, 265, 276, 292-317, 325-330, 338, 343, 350-351, 358-359, 363-367, 371-385, 389-392, 396-399, 403, 407-410, 416-418, 424, 428-429, 433-437, 441-450, 462-468, 473-477, 481, 485, 489-492, 496-497, 501-503, 509-511, 515, 519, 524-525, 529-530, 534-543, 547, 551-552, 585-664, 739-771, 775-798, 802-805, 818, 822, 830, 834, 840, 859-872, 891-892, 909-911, 934-942, 959-967, 975, 979, 983, 987, 991, 995, 1005-1047 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_io.py 130 98 25% 30, 76-79, 102-111, 209-248, 267-278, 303-308, 329-337, 358-366, 388-418, 426-435 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_random.py 9 3 67% 14-19, 29 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/_warnings.py 61 48 21% 86-102, 112-115, 126-150, 163-188, 196-199, 205-216 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/asserters.py 402 358 11% 92-142, 163-170, 176-177, 234-343, 352-377, 394-416, 420-435, 440-443, 471-504, 525-535, 539-542, 548-554, 560-564, 570-599, 633-679, 732-782, 877-1032, 1152-1224, 1253-1280, 1292-1313, 1317-1318, 1330-1336, 1350, 1358-1364, 1373-1378 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/compat.py 10 6 40% 10-14, 22-24 /home/admin/.local/lib/python3.8/site-packages/pandas/_testing/contexts.py 86 61 29% 46-47, 72-89, 114-134, 145-150, 173-184, 189-202, 206-213 /home/admin/.local/lib/python3.8/site-packages/pandas/_typing.py 149 33 78% 34-84, 204, 209, 213, 217, 223, 229, 233, 238, 243, 249, 253, 257, 262, 314 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11% 49-90, 121-157, 167-207 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/_arrow_utils.py 24 16 33% 17-20, 43-61 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/arrow/array.py 1003 805 20% 105-109, 117-120, 142-147, 154-159, 168-181, 234-245, 252-285, 294-348, 374-417, 423-429, 433, 437, 440, 443, 446, 449, 454-456, 459-460, 463-487, 490-536, 539, 542, 545-549, 556, 563, 573, 577-585, 589, 597, 655, 713, 723-732, 735-748, 751, 754, 766, 776, 785-834, 838-844, 860-861, 868-895, 898, 927, 936-944, 1006-1042, 1051-1081, 1091-1104, 1123-1151, 1168-1175, 1207-1225, 1251-1345, 1369-1424, 1438-1491, 1508-1530, 1548-1571, 1575-1588, 1613-1632, 1658-1685, 1689, 1698-1700, 1708-1718, 1723-1733, 1736-1739, 1742-1745, 1756-1764, 1767-1774, 1779-1781, 1786-1788, 1791-1804, 1807-1824, 1827, 1830-1832, 1835-1837, 1842-1846, 1853-1857, 1860, 1863, 1866, 1869, 1872, 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1487, 1501-1505, 1519-1523, 1549-1560, 1564-1570, 1573-1580, 1637-1638, 1799-1866, 1870, 1877, 1881-1888, 1903-1928, 1937, 1943, 1950, 1953-1967, 1974-1980, 1985-1993, 1997-2017, 2026, 2035, 2044, 2051, 2056, 2062, 2077-2091, 2100-2109, 2117-2147, 2152, 2157, 2178-2184, 2210-2221, 2241-2249, 2265-2267 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/datetimes.py 617 485 21% 90-91, 109-112, 117-148, 198, 263-265, 275-287, 291, 309-370, 389-502, 508-514, 517, 520-522, 529-531, 556, 569, 574, 584, 591, 595, 601-605, 615-634, 641-701, 709-713, 722-729, 733-748, 756-782, 794-797, 863-873, 1021-1058, 1071, 1111-1118, 1164-1190, 1244-1250, 1301-1307, 1319-1321, 1330, 1343-1345, 1381-1390, 1911-1917, 1971-1979, 2022-2134, 2172-2202, 2229-2256, 2281-2290, 2315-2344, 2370-2397, 2422-2440, 2446-2453, 2478-2486, 2521-2595 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/floating.py 34 3 91% 36, 40, 51 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/integer.py 70 9 87% 36, 40, 50-57 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/interval.py 638 486 24% 104, 220, 238-275, 288-293, 305-379, 389, 395-400, 458-460, 536-548, 608-631, 645-661, 674-677, 684, 688, 693, 699, 702, 706, 710, 715-731, 734-738, 742-817, 821, 825, 829, 833, 837, 841, 851-859, 864-878, 881-895, 926-935, 956-996, 999-1002, 1023-1033, 1043-1046, 1049, 1052-1077, 1133-1146, 1150-1168, 1171-1182, 1185-1205, 1225, 1233-1260, 1266-1270, 1273-1274, 1284-1286, 1293-1295, 1302, 1309-1313, 1372-1385, 1396, 1438-1444, 1466-1474, 1487-1498, 1504-1550, 1573-1577, 1582-1591, 1608-1613, 1616-1624, 1632-1635, 1683-1686, 1691-1720, 1724-1730, 1736-1748, 1753-1757, 1776-1796 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/masked.py 572 451 21% 88-90, 121-134, 140-141, 145, 149, 153, 158-167, 173-198, 204, 214-231, 234-248, 251-264, 267, 271, 275, 278-280, 283-285, 288-290, 294-296, 300, 326-330, 336, 339, 342, 345, 415-439, 443-446, 450, 454, 458, 461-504, 513, 521-592, 598-600, 609, 614-625, 628-730, 735-773, 782-821, 824, 828, 832, 840-842, 854-876, 881-898, 901-904, 914-915, 924-932, 939-971, 975, 994-1021, 1025-1037, 1050-1078, 1084-1097, 1100-1109, 1119-1135, 1147-1155, 1160-1167, 1174-1182, 1189-1197, 1202-1203, 1211-1212, 1281-1298, 1362-1380, 1385-1391 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numeric.py 152 112 26% 41, 52, 56, 60, 64, 72-114, 118, 125-136, 145, 149-235, 248-263, 267-268, 274-280, 286-289 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/numpy_.py 186 130 30% 80-95, 101-120, 123, 130, 136, 143-188, 194-202, 205, 208-211, 214-218, 231-233, 243-245, 250-254, 259-263, 273-277, 287-291, 302-304, 315-319, 331-335, 347-351, 363-367, 378-382, 393-397, 408-420, 426, 429, 432, 435, 438-459, 466-472 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/period.py 420 311 26% 83-92, 105-107, 179, 215-236, 247-249, 259-274, 280, 297-298, 302-319, 331-338, 341, 344-346, 353, 361, 364-370, 376-397, 479, 497-536, 541, 589-609, 615-617, 626-643, 650-664, 672-677, 680-688, 708-712, 715-718, 731-740, 754-781, 803-818, 837-847, 912-943, 948, 953, 977-993, 1018-1033, 1037-1081, 1094-1128, 1132-1143 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/accessor.py 110 78 29% 20, 30-31, 34, 46-47, 50, 53-58, 102-108, 185-190, 216-218, 232-234, 265-287, 309-312, 334-355, 362-363, 367-386 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/array.py 786 648 18% 107-123, 155-158, 180-266, 275-287, 378-497, 506-510, 538-554, 557-579, 585-586, 590, 594, 604, 618, 622, 631, 635, 642-645, 649-651, 654, 658, 661-664, 668, 682, 696, 701-706, 746-772, 775-799, 809-820, 823-834, 838, 849-853, 868-893, 900, 907, 913-1001, 1004-1012, 1017-1030, 1035-1097, 1100-1120, 1128-1133, 1136-1137, 1143-1195, 1254-1274, 1316-1326, 1336, 1341-1344, 1351-1361, 1364-1367, 1374-1384, 1398-1405, 1419-1426, 1454-1470, 1490-1498, 1512-1521, 1538-1539, 1556-1557, 1572-1592, 1595-1618, 1621-1624, 1627-1630, 1639-1702, 1709-1739, 1742-1765, 1774-1782, 1785, 1788, 1791, 1794, 1800-1803, 1808, 1832-1870, 1875, 1880, 1885-1892 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/sparse/dtype.py 143 94 34% 39, 85-99, 104, 109-140, 156, 159-160, 178, 182, 186, 193, 197, 201, 205, 208, 219-221, 250-273, 297-307, 311-317, 357-370, 396-398, 403-426 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_.py 257 193 25% 58-65, 105, 110-120, 124, 154-165, 180-185, 193-215, 225-227, 315-320, 324-336, 340-365, 371, 375-377, 383-390, 393-396, 399-429, 435, 438-468, 473-476, 479-483, 486-490, 493-497, 500-503, 512-517, 520-547, 560-608 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/string_arrow.py 200 142 29% 53-55, 112-116, 128, 132-152, 158, 165, 168-170, 174-185, 188-201, 204-216, 231-283, 288-306, 309-310, 313-314, 325-331, 336-338, 343-345, 348-349, 352-353, 356-357, 360-361, 364-365, 368-369, 372-373, 376-377, 380-381, 384-385, 388, 391, 394-398, 401-405, 408-412 /home/admin/.local/lib/python3.8/site-packages/pandas/core/arrays/timedeltas.py 440 333 24% 78, 83-96, 142, 164-167, 186, 197-199, 207-214, 218-227, 242-271, 279-319, 325-331, 334, 338, 348-368, 371-384, 400-407, 419-426, 432-441, 447-449, 454-460, 466-467, 473-500, 509-550, 553-559, 567-581, 586-610, 615-631, 635-660, 664-678, 683-685, 690-692, 697-702, 707-712, 715-718, 721, 725, 784-785, 795, 826-853, 896-966, 985-1006, 1039-1042, 1046-1062 /home/admin/.local/lib/python3.8/site-packages/pandas/core/base.py 322 197 39% 75-82, 113, 120, 126-131, 138-144, 163, 172-178, 196-200, 204-207, 212, 217-230, 233-246, 261, 264, 283, 288, 299-300, 320, 324, 331, 348-350, 357, 364, 429, 526-561, 566, 608-610, 666-678, 722-724, 730-742, 763, 780-784, 799, 802, 818-823, 849-926, 1015, 1025-1031, 1068-1071, 1082, 1093-1095, 1106-1108, 1135-1144, 1164-1177, 1293, 1302, 1311-1323, 1331-1333, 1337, 1340-1350, 1357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/common.py 195 126 35% 57, 77-81, 85-92, 126-138, 141-145, 164, 176, 183, 190, 197, 204, 211, 221, 226, 230-258, 276-287, 291-293, 301, 310, 322, 334, 342, 352-364, 379, 405-413, 418, 425, 453-478, 510-518, 527-533, 543-548, 563-568, 576, 626, 634, 653 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/align.py 99 76 23% 32-35, 42-51, 57, 64, 71-80, 87-142, 149-165, 188-213 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/check.py 8 1 88% 8 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/common.py 29 23 21% 14-16, 24-48 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/engines.py 50 23 54% 23, 37-42, 53-55, 63, 77-82, 88, 113-121, 134, 137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/eval.py 111 90 19% 27, 51-72, 88-89, 95-99, 119-120, 147-149, 153-167, 299-413 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expr.py 361 213 41% 65-66, 84-91, 114-117, 165-166, 262, 314, 397-401, 404-415, 418-421, 424, 428-451, 454-459, 462-482, 491, 504-532, 535-537, 540, 543-545, 548, 551, 554, 557, 560-561, 564-565, 571, 574-591, 595-605, 617-635, 638-655, 658-706, 709, 712-730, 733-735, 738-746, 768, 776, 804-809, 813, 816, 819, 822, 828, 835-837 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py 107 70 35% 24, 46, 58-61, 68-70, 75-89, 93-130, 171, 176-188, 196-199, 211-220, 235-240, 255-256, 267-268, 272-273, 281-283 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/ops.py 293 182 38% 72-75, 81-87, 91, 94, 97, 100, 103-117, 129-135, 139, 143-152, 158, 162-167, 171, 175, 179, 183, 188, 191, 195, 200, 214-216, 219, 226-227, 232-234, 238-240, 244, 248, 252-257, 265-273, 281-289, 346-355, 359, 374-387, 405-408, 427-457, 464-490, 493-511, 515, 529-539, 565-571, 577-579, 582, 586-593, 598-599, 603-605, 608-609, 614-617, 620 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/parsing.py 45 33 27% 35-67, 90-93, 125-130, 159-164, 181-195 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/pytables.py 352 257 27% 49-50, 57-61, 64, 68-78, 83, 88-89, 92, 103-106, 109, 112-151, 155-159, 164, 172, 177, 182, 187, 191-192, 200-256, 259, 266-268, 272-278, 282, 285-310, 313-316, 321, 324, 329, 336, 342, 345-368, 373-374, 379-393, 401-404, 411-416, 419, 422-425, 430-444, 449-470, 473, 476, 497-503, 545-585, 588-590, 594-609, 616-619, 623-631, 636-641 /home/admin/.local/lib/python3.8/site-packages/pandas/core/computation/scope.py 125 84 33% 36-40, 49-53, 60, 76-82, 88-89, 118-120, 153-188, 191-193, 207, 226-246, 261-271, 285-294, 304-314, 330-338, 343, 356-357 /home/admin/.local/lib/python3.8/site-packages/pandas/core/config_init.py 197 20 90% 40-42, 54-56, 68-70, 290-292, 307-310, 345, 421-423, 641-643, 664-672 /home/admin/.local/lib/python3.8/site-packages/pandas/core/construction.py 218 142 35% 67-71, 290-379, 386, 393, 442-450, 453, 464-466, 469-471, 480-491, 520, 524, 530-532, 535-536, 539-542, 548-553, 557-559, 564, 572-576, 580-581, 596, 599, 622-633, 641, 656, 662-679, 696-699, 710, 734-767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/api.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/astype.py 114 92 19% 40, 50, 57, 83-140, 149-159, 177-193, 220-251, 266-306 /home/admin/.local/lib/python3.8/site-packages/pandas/core/dtypes/base.py 140 59 58% 33-36, 104, 124-133, 138, 141, 152, 164, 180, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/generic.py 2254 1609 29% 193-200, 279, 292-311, 331-333, 352, 357, 448-451, 457-467, 478, 488, 503-507, 514-515, 520-521, 538, 543-569, 573-579, 590-595, 601, 605, 612, 621, 644, 670, 708, 714-721, 729-731, 744-780, 848-850, 853-856, 962-963, 987-1042, 1055, 1068, 1081, 1216-1256, 1313-1324, 1331, 1414-1417, 1424-1438, 1442-1453, 1457-1462, 1466, 1507-1518, 1588-1589, 1593, 1597, 1629-1631, 1689, 1723-1735, 1775-1778, 1782-1792, 1825-1872, 1891, 1905, 1917-1918, 1922, 1927, 1987, 1997-2010, 2016, 2023-2024, 2035-2067, 2075-2076, 2084-2087, 2095-2100, 2237-2252, 2522-2532, 2678-2682, 2876-2878, 2953-2955, 3032-3034, 3113-3118, 3145, 3172, 3356-3459, 3515-3533, 3560, 3587, 3761-3772, 3798, 3817-3824, 3827, 3907-3909, 3922-3938, 3948-3952, 4064, 4067-4078, 4083-4131, 4134, 4142-4151, 4158-4159, 4172-4174, 4202-4249, 4255-4286, 4293-4294, 4353-4356, 4362, 4466-4474, 4488, 4502, 4516, 4529-4555, 4582-4642, 4657-4660, 4721-4734, 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3036-3093, 3144-3310, 3376-3394, 3451-3453, 3527-3546, 3564-3569, 3582-3587, 3602-3610, 3627-3635, 3686-3774, 3804-3820, 3844-3861, 3884-3902, 3939-3940, 3978-3983, 4000-4006, 4040-4104, 4202-4235, 4247-4258, 4282-4292 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/grouper.py 405 322 20% 54, 250-254, 265-278, 294-309, 332-395, 400-409, 414-420, 425-431, 436-442, 447-454, 458-465, 520-611, 614, 617, 621, 625-639, 646-654, 658, 663-667, 671, 679-686, 692-698, 702-720, 725-783, 787, 822-1019, 1023, 1027-1044 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/indexing.py 105 78 26% 24-28, 114-120, 126-149, 152-155, 158-169, 172-184, 187-226, 230-235, 239-244, 250, 283-284, 293, 300, 303 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/numba_.py 52 39 25% 45-56, 93-119, 153-179 /home/admin/.local/lib/python3.8/site-packages/pandas/core/groupby/ops.py 533 383 28% 99, 121-123, 156-179, 197-220, 234-265, 268-284, 287-296, 312-322, 337-381, 386-397, 408-419, 434-461, 475-497, 519-615, 631-657, 692-697, 701, 705, 708, 712, 725-727, 736-737, 742-748, 754-782, 787-792, 802-819, 824, 828, 832, 839-845, 850-855, 861, 869, 873-878, 883-884, 891-897, 902, 906-908, 912-917, 925-935, 953-959, 987-1001, 1007-1025, 1068-1074, 1081-1086, 1091, 1096-1100, 1111-1125, 1129-1137, 1141-1151, 1160, 1164-1167, 1171, 1175, 1179-1185, 1189-1196, 1211-1216, 1221, 1226, 1229-1239, 1243, 1246, 1252-1254, 1264-1266, 1272-1278 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/objects.py 130 95 27% 55-59, 70, 85-100, 119, 140-142, 153-213, 228, 268-282, 315-318, 336-375, 390 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexers/utils.py 148 122 18% 29-30, 54-57, 94-99, 114-118, 152-186, 226-234, 270-274, 278-279, 282-284, 300-331, 342-343, 358-370, 390-396, 403-414, 513-555 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/accessors.py 140 92 34% 43, 56-64, 67-80, 85-114, 117, 123-144, 163-170, 173-191, 194-210, 213, 216-229, 332, 336, 368, 438, 467, 475, 554-580 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/api.py 126 97 23% 94-95, 103-109, 138-157, 174-191, 210-308, 332-346, 362-364 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/base.py 2276 1712 25% 187-193, 259-267, 279-297, 366-372, 379-386, 393-400, 407-414, 433, 482, 488, 492-495, 499, 502, 505-513, 516, 518, 522, 528, 533-535, 541, 544, 548, 552-557, 572, 575, 580, 589-606, 609-611, 614-616, 622-625, 658, 671-681, 685, 701-706, 725-738, 756-758, 764-767, 774-776, 800-807, 818, 827-834, 840, 842, 844, 851, 865, 885, 888-919, 925-929, 951, 956-981, 1007-1038, 1080, 1082, 1095, 1109-1117, 1165-1170, 1202-1208, 1212, 1222, 1232-1243, 1252, 1259, 1266-1275, 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4818-4863, 4872-4885, 4892-4900, 4925, 4930-4935, 4971, 4984, 4992-4999, 5006-5010, 5014-5018, 5053-5058, 5066, 5081-5091, 5099-5101, 5107, 5147-5148, 5157, 5177-5203, 5209-5210, 5222-5228, 5242-5258, 5264-5268, 5283-5312, 5373-5399, 5412, 5479-5496, 5531-5541, 5606-5624, 5631, 5689, 5731, 5734-5737, 5744, 5803-5837, 5858-5861, 5867-5894, 5917-5941, 5947, 5953, 5959, 5988-5999, 6009, 6020-6057, 6065-6081, 6089-6099, 6105-6111, 6130-6138, 6159-6187, 6198-6208, 6291-6293, 6298, 6344-6352, 6365, 6373-6376, 6401-6408, 6411-6422, 6442-6484, 6517-6572, 6603-6612, 6629-6664, 6690-6701, 6712-6735, 6744-6785, 6789-6795, 6799-6804, 6807-6817, 6821-6822, 6825, 6828, 6831, 6835, 6876-6882, 6923-6929, 6936-6944, 6948-6956, 6960-6968, 6972-6993, 6997-7018, 7029, 7062-7069, 7105, 7109-7110, 7113, 7119, 7122-7124, 7128, 7135-7140, 7155-7159, 7163-7164, 7174, 7178, 7196-7199, 7215-7228, 7232-7243 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/category.py 123 74 40% 178, 182, 194, 213-223, 246-273, 285-296, 303, 311-319, 322-326, 332, 337-340, 356-368, 376-381, 384-396, 401, 470-471, 475-486 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimelike.py 384 272 29% 73, 91, 95, 100, 104, 109, 114, 119, 125, 131-161, 165-170, 173-174, 189-200, 206, 212, 218-226, 230-234, 243, 247, 251-269, 273-277, 295-321, 340-354, 386, 392-401, 423, 437-438, 441-442, 447, 451-473, 478, 487-490, 493, 496-512, 516-519, 523-526, 532-548, 552-568, 572-586, 592-616, 622-651, 655-667, 676-679, 684-687, 691, 695-696, 705-721, 727-748, 752-754, 758-762, 776-787 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/datetimes.py 285 213 25% 64, 75-100, 254, 264-265, 269-270, 279-280, 284-287, 291-292, 296-297, 301, 320-354, 367-372, 375-376, 382-386, 393-396, 404-416, 428-445, 456-471, 491-510, 513-523, 530-534, 544-586, 591-598, 617-659, 667, 690-705, 731-755, 942-956, 1031-1049, 1063-1064 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/extension.py 83 37 55% 28-29, 62, 71-78, 81, 90, 96-105, 154, 160-171, 177, 188, 191-192 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/frozen.py 44 21 52% 45-47, 63-65, 74-76, 79-81, 84-86, 91, 96, 100, 106, 109, 112 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/interval.py 376 267 29% 113-123, 127-137, 145, 221-232, 263-267, 299-303, 334-336, 344-348, 363-373, 377, 380-386, 391, 398, 408, 415-432, 482, 502-506, 525-571, 574-597, 639-667, 676-693, 699-724, 733-736, 744-768, 772, 779-790, 798, 801, 804-807, 813, 817, 821, 825, 833, 839, 844, 854-867, 882-889, 907-918, 926, 932, 941, 955-957, 1059-1137 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/multi.py 1380 1162 16% 105, 143-153, 184-194, 206-211, 326-359, 377-383, 407-445, 489-510, 561-597, 645-658, 717-722, 729-750, 754, 766, 776-779, 782, 790, 800-804, 818-846, 931-940, 956, 972, 979, 990-1016, 1074-1078, 1089-1113, 1119, 1123-1125, 1128-1137, 1180-1204, 1208, 1212-1214, 1218-1223, 1227, 1232-1235, 1243, 1248, 1261-1270, 1279-1280, 1285-1316, 1328-1390, 1396, 1425-1454, 1479, 1482-1506, 1513-1537, 1545, 1550, 1554-1557, 1567, 1571-1580, 1599-1605, 1655-1657, 1661-1665, 1727-1755, 1787, 1823, 1835-1837, 1873-1898, 1941-1989, 1996-2002, 2007-2032, 2044-2050, 2067-2086, 2113-2137, 2140-2144, 2148-2152, 2184-2221, 2226-2242, 2287-2298, 2334-2344, 2357-2363, 2425-2480, 2483-2496, 2499-2506, 2512-2516, 2524, 2529-2539, 2542-2559, 2565-2569, 2619-2621, 2677, 2680-2727, 2748-2752, 2795-2872, 2915-2926, 2934-3075, 3085-3188, 3226-3319, 3343-3407, 3435-3447, 3463-3514, 3521-3527, 3533-3564, 3567, 3575-3579, 3587-3596, 3599-3600, 3603-3608, 3611-3627, 3633-3644, 3647-3658, 3674-3696, 3714-3731, 3743-3744, 3753-3768, 3804-3808, 3812-3836, 3840-3845, 3862-3878, 3897-3901, 3908-3918 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/period.py 191 122 36% 61-67, 157, 162, 175-176, 180-181, 186, 191, 196, 211-265, 272, 291-308, 314-322, 338-343, 351-356, 362, 372-378, 400-440, 443-455, 458-463, 467-470, 473-474, 478-482, 537-547 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/range.py 508 388 24% 103, 117-142, 155-161, 183-188, 213-214, 217-219, 228-231, 235, 239-245, 277-278, 306, 315, 319, 323, 326-331, 335, 342-351, 360-381, 388, 393, 397, 401-413, 416-418, 422-424, 427-433, 437-439, 443-445, 460-471, 478-483, 489-491, 500-526, 534-575, 579-580, 589-597, 602-608, 630-683, 687-770, 773-782, 791-808, 811-829, 840-890, 900, 906-924, 930-931, 935-947, 953, 956, 961-964, 975-1037 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexes/timedeltas.py 69 38 45% 110, 121, 136-176, 184, 197-204, 208-209, 213-215, 221, 308-315 /home/admin/.local/lib/python3.8/site-packages/pandas/core/indexing.py 917 749 18% 83, 137, 548, 613, 661, 676-683, 689-716, 728-775, 789-829, 833-849, 871, 879-893, 900-910, 919-921, 926-928, 932-940, 950-965, 970-1026, 1033-1084, 1087, 1093-1097, 1106, 1109, 1112, 1115, 1120-1123, 1144-1158, 1161, 1173-1192, 1215-1219, 1238-1242, 1269-1273, 1278-1289, 1293, 1297-1307, 1310-1343, 1350-1362, 1379-1434, 1459-1464, 1479-1518, 1529-1554, 1566-1569, 1589, 1594-1598, 1617-1621, 1625, 1627, 1633, 1636, 1639, 1642-1643, 1647, 1653, 1662-1669, 1675, 1679-1685, 1701-1837, 1844-1928, 1933-1950, 1953-1999, 2011-2036, 2042-2078, 2084-2181, 2188-2198, 2217-2299, 2302-2348, 2360, 2363-2371, 2374-2386, 2401-2404, 2409-2410, 2413-2419, 2422-2430, 2441-2444, 2462-2464, 2471-2473, 2502-2525, 2533-2541, 2549-2552, 2559-2562, 2572-2579, 2589, 2602, 2618, 2627 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/dataframe_protocol.py 101 1 99% 158 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/from_dataframe.py 171 151 12% 48-54, 73-91, 108-137, 156-164, 181-216, 233-307, 313-340, 357-376, 411-436, 469-499 /home/admin/.local/lib/python3.8/site-packages/pandas/core/interchange/utils.py 44 11 75% 75-90 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/api.py 36 26 28% 50-81, 88-97 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/array_manager.py 583 443 24% 130, 134-138, 142, 150, 155, 160-161, 165-167, 170, 176, 179, 182-183, 186-193, 216-255, 261-310, 313-319, 327, 330, 333-339, 347-348, 351, 354-361, 366-370, 375-378, 381-401, 404, 407-411, 423-425, 434, 438, 442, 447, 453, 457, 460-468, 479, 490, 509-529, 544-545, 578-632, 644-660, 665-679, 686-689, 698, 708-715, 718-736, 756-768, 771-781, 787-788, 794, 802, 822-861, 872-881, 894-916, 922-927, 944-969, 983-1001, 1008-1013, 1023-1034, 1052-1089, 1115-1144, 1158, 1166-1177, 1180-1185, 1192, 1196-1199, 1203, 1207, 1211, 1215, 1219, 1223, 1227-1230, 1234-1238, 1242, 1245, 1248-1253, 1256-1258, 1261-1265, 1276-1278, 1284-1289, 1293-1296, 1305, 1311-1314, 1331, 1335, 1351-1361 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/base.py 88 40 55% 44, 48, 52, 61-70, 85, 98-100, 114, 121-130, 138, 142, 148, 151, 154, 160, 181-193, 196-201, 205, 222 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/blocks.py 1041 770 26% 120-121, 135-139, 167, 175-178, 186, 190, 201-203, 211, 221-228, 241, 249-256, 260, 273-277, 288-293, 298-299, 315-318, 329-331, 337-348, 353-369, 376-385, 402-408, 422-424, 430-447, 457-459, 472-474, 509-526, 531-532, 537-544, 567-647, 678-701, 715-806, 839-862, 878, 884, 890, 906, 922-924, 939-940, 954, 961, 991-1007, 1034-1068, 1087-1141, 1160-1260, 1275-1311, 1333-1393, 1398-1399, 1412-1433, 1459-1467, 1484-1500, 1512-1551, 1556, 1563, 1570, 1573, 1608-1639, 1645-1709, 1715-1778, 1782-1789, 1793, 1799-1803, 1806, 1819-1825, 1854-1870, 1875-1877, 1888-1905, 1910-1912, 1919-1934, 1944-1976, 1981, 1985, 2006-2025, 2034-2035, 2040-2041, 2052-2053, 2070-2094, 2103, 2107, 2115, 2134, 2140, 2163-2166, 2171-2173, 2181-2186, 2197, 2211-2238, 2271-2298, 2325, 2329, 2354, 2356, 2358, 2361, 2364, 2379-2382, 2421, 2429, 2434-2440, 2450-2459, 2467-2478, 2486-2494, 2507-2581, 2593-2607 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/concat.py 350 303 13% 68-69, 90-117, 139-173, 194-252, 262-290, 303-319, 336-395, 402-406, 409, 413-418, 422-428, 435-458, 462-487, 492-569, 576-615, 622-638, 651-668, 678-681, 707, 720-738, 749-791 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/construction.py 428 290 32% 128-140, 147, 156-159, 174-194, 209-230, 244-388, 400-408, 429-464, 479, 496, 503, 529-560, 567-571, 584-591, 596-605, 622, 633-634, 636-637, 641-642, 645, 648, 650, 655, 658, 663-668, 682-703, 707-721, 730-739, 766-768, 794-804, 807-821, 825-827, 831-838, 848, 861-883, 909-920, 935-937, 972, 987-994, 1042, 1044-1063 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/managers.py 951 659 31% 155, 161, 165, 190, 196-209, 212, 219-221, 225-226, 235, 243-244, 252, 259-266, 273-274, 277-278, 295-304, 327-356, 359-365, 374, 386-394, 397-403, 413-414, 417, 422-426, 429-433, 443-451, 460-468, 471-475, 484, 494-505, 512, 516, 521, 526-536, 539-540, 553-565, 574-580, 586-614, 618, 634-665, 675-681, 715, 721-727, 734, 737, 740, 795-901, 908-927, 992-1002, 1005-1011, 1052-1082, 1106-1108, 1121-1136, 1148-1279, 1299-1326, 1340-1353, 1364-1385, 1398-1427, 1441-1444, 1453-1466, 1472-1479, 1496-1515, 1530-1539, 1545, 1552, 1580-1592, 1610-1655, 1670-1675, 1703-1704, 1717, 1724, 1727, 1729-1730, 1737, 1761-1762, 1764, 1766, 1781-1800, 1829-1832, 1840, 1869-1871, 1880-1881, 1887-1892, 1900, 1903-1919, 1922-1939, 1942, 1951, 1956, 1960-1979, 1984-1994, 1998, 2002, 2005, 2009, 2017, 2020-2022, 2026, 2038-2042, 2050-2054, 2061, 2073-2074, 2082-2086, 2106-2116, 2139-2140, 2153-2169, 2184, 2195-2196, 2213, 2217, 2221-2235, 2241, 2267-2276, 2282-2311, 2316-2320, 2326-2343 /home/admin/.local/lib/python3.8/site-packages/pandas/core/internals/ops.py 62 46 26% 12-15, 33-52, 61-86, 93-95, 107-136, 143-147 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/describe.py 148 115 22% 46, 80-95, 108, 127-130, 153-159, 162-176, 180-196, 201-207, 220-247, 263-279, 295-327, 341-351, 364-373, 387-408 /home/admin/.local/lib/python3.8/site-packages/pandas/core/methods/selectn.py 120 97 19% 34, 42-47, 50, 54, 58, 67-69, 88-156, 176-182, 185-262 /home/admin/.local/lib/python3.8/site-packages/pandas/core/missing.py 314 268 15% 46, 53-61, 81-118, 123-140, 168-183, 204-224, 247-267, 302-359, 366-381, 408-489, 507-566, 605-611, 650-654, 734-740, 765-787, 818-858, 866-870, 880-888, 899-901, 910-912, 917-924, 929-936, 943-946, 950, 984-1013, 1028-1030 /home/admin/.local/lib/python3.8/site-packages/pandas/core/nanops.py 626 505 19% 71, 83, 88-104, 130-160, 167-182, 186-195, 202-217, 253-261, 310-350, 354-356, 361-397, 415-429, 451-462, 474-494, 533-551, 588-606, 643-655, 664-674, 711-740, 768-821, 844-848, 880-895, 934-941, 981-1017, 1058-1070, 1083-1097, 1145-1149, 1191-1195, 1237-1285, 1327-1384, 1419-1427, 1439-1456, 1483-1497, 1513-1545, 1568-1576, 1581-1585, 1599-1614, 1620-1643, 1657-1671, 1675-1700, 1708-1720, 1747-1767 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/__init__.py 184 134 27% 78, 133-150, 163-173, 183-201, 233-334, 343-365, 384-409, 417-433, 446-473, 489-494 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/array_ops.py 191 155 19% 68-83, 99-139, 164-188, 217-234, 256-298, 302-339, 358-405, 424, 430, 433, 435, 448, 470-519, 537-542 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/common.py 56 34 39% 64, 69-81, 101-105, 128-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/dispatch.py 6 1 83% 26 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/docstrings.py 57 2 96% 50, 60 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/invalid.py 17 9 47% 30-37, 54-55 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/mask_ops.py 59 52 12% 42-73, 106-126, 156-184, 188-189 /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/methods.py 34 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/ops/missing.py 56 48 14% 49-73, 105-134, 158-180 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/api.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/concat.py 284 246 13% 54-58, 78, 95, 112, 129, 146, 364-385, 406-563, 570-623, 626-629, 632-633, 639-640, 653-699, 702-705, 709, 713-821 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/encoding.py 154 138 10% 147-224, 236-334, 447-533 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/melt.py 138 117 15% 33-35, 50-158, 214-248, 489-540 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/merge.py 919 816 11% 104-106, 128, 148-162, 176-217, 324-358, 616-633, 680-747, 760-803, 806-825, 830-831, 835-840, 847-869, 872-884, 906-927, 935-1034, 1038, 1046-1101, 1125-1135, 1155-1276, 1285-1440, 1461-1463, 1471-1558, 1562-1605, 1642-1690, 1737-1779, 1799-1800, 1816-1842, 1846-1847, 1859-1864, 1891-1898, 1914-1990, 1996-2063, 2068-2189, 2204-2234, 2240-2242, 2247, 2275-2279, 2285-2309, 2369-2468, 2475-2505, 2511-2517, 2527-2554, 2558-2560, 2564, 2568-2575, 2591-2645 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/pivot.py 366 333 9% 51, 71-110, 129-255, 269-339, 345-362, 368-436, 448-480, 484-494, 506-562, 671-734, 740-814, 818-831, 864-885 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/tile.py 181 158 13% 241-305, 369-389, 403-472, 481-505, 522-539, 557-561, 568-591, 602-608, 617-625, 632-640, 647-651 /home/admin/.local/lib/python3.8/site-packages/pandas/core/reshape/util.py 28 22 21% 33-60, 77-82 /home/admin/.local/lib/python3.8/site-packages/pandas/core/roperator.py 29 15 48% 11, 15, 19, 23, 27, 31, 38-42, 46, 50, 54, 58, 62 /home/admin/.local/lib/python3.8/site-packages/pandas/core/sample.py 58 48 17% 19, 31-76, 90-113, 144-151 /home/admin/.local/lib/python3.8/site-packages/pandas/core/series.py 1122 756 33% 179-187, 221-230, 384, 394-519, 546-570, 576, 584-586, 591, 605, 619, 716, 754-756, 762, 778-781, 787, 857-863, 916-921, 938, 945-960, 971, 985, 990, 997, 1001, 1004, 1009-1033, 1037-1073, 1077-1093, 1096-1097, 1113, 1121-1138, 1141-1219, 1222-1225, 1230-1248, 1251-1256, 1259-1263, 1280-1290, 1298, 1302-1305, 1311-1312, 1320, 1325, 1331-1336, 1346-1367, 1376, 1429-1432, 1446, 1458, 1470, 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/home/admin/.local/lib/python3.8/site-packages/pandas/core/shared_docs.py 13 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/sorting.py 252 201 20% 47-49, 81-106, 144-200, 223-224, 228-232, 239-254, 276-287, 293-301, 329-366, 399-400, 410, 417, 436-439, 458-473, 480-484, 516-533, 549-577, 587-594, 606-623, 656-670, 681-692, 710-725 /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/core/strings/accessor.py 581 386 34% 49, 123-129, 140-141, 179-195, 220-236, 239-240, 251-393, 411-448, 593-683, 888-895, 912-913, 1008-1009, 1022-1023, 1085-1086, 1151-1152, 1281-1290, 1321-1322, 1354-1355, 1485-1508, 1551-1552, 1613-1625, 1650, 1655, 1660, 1723-1728, 1802-1803, 1878-1879, 1898-1907, 1925-1926, 2014-2015, 2023-2024, 2032-2033, 2088-2089, 2096-2097, 2155-2156, 2197-2198, 2224-2225, 2292-2293, 2359-2363, 2429-2433, 2525-2526, 2612-2654, 2732, 2771-2776, 2788-2793, 2812-2813, 2855-2860, 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/home/admin/.local/lib/python3.8/site-packages/pandas/io/_util.py 6 2 67% 9-10 /home/admin/.local/lib/python3.8/site-packages/pandas/io/api.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/clipboards.py 11 1 91% 20 /home/admin/.local/lib/python3.8/site-packages/pandas/io/common.py 444 337 24% 124-132, 135, 138, 154-156, 161, 166, 183-185, 189-214, 219, 226, 252-260, 268-270, 278, 319-458, 480-482, 522-531, 560-586, 598-600, 615, 630, 645, 702-913, 936, 939-951, 963-969, 977-984, 991-999, 1003-1006, 1017-1025, 1032-1037, 1041-1042, 1053, 1056, 1059-1061, 1064-1066, 1069-1071, 1078-1084, 1087, 1090-1100, 1107-1134, 1139-1148, 1154-1167, 1175-1187, 1209-1212, 1233-1253 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/__init__.py 9 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_base.py 413 301 27% 395, 434, 473-515, 523-545, 552, 556, 559-569, 574, 578, 582, 586, 589-591, 596-597, 620-626, 655-690, 716-897, 1103-1121, 1129, 1134, 1192-1233, 1240, 1247, 1254, 1257, 1260-1264, 1280-1298, 1306-1310, 1314, 1322, 1326-1327, 1368-1401, 1470-1513, 1516, 1551, 1575, 1579, 1583, 1586, 1594 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odfreader.py 141 113 20% 25, 44-45, 49-51, 54-56, 61, 66-69, 72-76, 79-89, 97-158, 166-168, 171-173, 179-183, 186-222, 229-249 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_odswriter.py 138 115 17% 27, 46-62, 71, 76-82, 88-90, 103-149, 164-171, 186-219, 241-278, 293-335 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py 242 189 22% 35-36, 56-88, 97, 102-103, 109-112, 138-148, 172-177, 207-225, 243, 270-306, 325-339, 366-379, 400-402, 420, 437-439, 450-529, 548-549, 553-555, 558-560, 566, 569-570, 573-574, 577-592, 597-626 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_pyxlsb.py 56 38 32% 34-37, 41-43, 46-52, 56, 59-60, 63-66, 71-80, 87-112 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_util.py 102 71 30% 24-27, 43, 65-86, 90-93, 115-125, 149-158, 163, 168, 173, 178, 197-209, 214, 219, 223-236, 260-271, 295-305, 327-332 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlrd.py 62 43 31% 33-35, 39-41, 44-50, 54, 57-58, 61-62, 67-126 /home/admin/.local/lib/python3.8/site-packages/pandas/io/excel/_xlsxwriter.py 83 63 24% 101-172, 192-210, 219, 223-224, 230, 241-275 /home/admin/.local/lib/python3.8/site-packages/pandas/io/feather_format.py 43 28 35% 54-96, 139-162 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/__init__.py 4 2 50% 5-8 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/console.py 33 28 15% 15-47, 63-76, 87-94 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/format.py 908 752 17% 112, 209-214, 217-231, 234, 243-261, 279-295, 298-322, 325-363, 366-374, 377, 386-421, 426, 429, 432, 435, 442-451, 457-460, 468-476, 480-484, 504-510, 537-549, 585-609, 615-621, 625, 631, 635, 639, 643, 647, 651, 655, 659, 663, 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820, 830-835, 838, 859, 863-864, 871-872, 880-883, 894-900, 903-911, 914-921, 925-926, 930-931, 945-948, 952-960, 965-967, 970, 974, 982, 991-992, 996-997, 1011, 1014-1016, 1021, 1025, 1039-1043, 1057-1060, 1064-1072, 1075, 1080-1082, 1086, 1090, 1101 /home/admin/.local/lib/python3.8/site-packages/pandas/io/formats/printing.py 203 183 10% 43-59, 66-71, 107-130, 140-161, 195-234, 240-241, 245-275, 279, 320-456, 481-497, 504 /home/admin/.local/lib/python3.8/site-packages/pandas/io/gbq.py 21 14 33% 12, 18-23, 173-186, 214-215 /home/admin/.local/lib/python3.8/site-packages/pandas/io/html.py 367 291 21% 54, 67-80, 105, 127-134, 150-161, 242-247, 257-258, 278, 294, 310, 326, 341, 360, 375, 401, 420, 431, 458-476, 501-558, 576-579, 603-606, 609-630, 633-634, 637, 640, 643, 646, 649-652, 655, 658-661, 664-679, 698-702, 728-729, 732, 737, 740-765, 768, 786-824, 827-842, 845-848, 851, 855-862, 866-886, 918-935, 939-940, 944-968, 972-1021, 1192-1212 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_json.py 501 386 23% 84, 107, 127, 146-204, 222-237, 240, 243-244, 266-269, 272-273, 281-286, 292-301, 327-379, 383, 408, 433, 458, 483, 743-784, 817-862, 872-878, 894-919, 925, 931, 935, 939, 946-981, 987-1008, 1017-1018, 1021, 1025, 1029, 1033, 1036-1061, 1064, 1072, 1099-1122, 1128-1131, 1134-1141, 1144, 1150-1160, 1163, 1176-1241, 1251-1280, 1283, 1291-1298, 1301-1307, 1315-1346, 1354-1374, 1377-1382, 1387-1415 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_normalize.py 142 126 11% 35-39, 86-120, 146-166, 184-191, 237-244, 388-536 /home/admin/.local/lib/python3.8/site-packages/pandas/io/json/_table_schema.py 134 114 15% 42-43, 79-96, 101-120, 124-151, 195-224, 283-316, 355-382 /home/admin/.local/lib/python3.8/site-packages/pandas/io/orc.py 52 38 27% 80-97, 162-205 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parquet.py 168 131 22% 46-75, 88-115, 121-122, 125, 128, 133-141, 153-194, 205-237, 244-247, 259-287, 299-342, 405-425, 493-509 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/arrow_parser_wrapper.py 67 53 21% 21-25, 31-42, 48-79, 101-136, 149-164 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/base_parser.py 578 491 15% 96, 109-176, 202-234, 240, 245, 253-267, 294-340, 349-352, 359-383, 387-406, 410-434, 438-451, 455-503, 515-593, 617-662, 683-766, 784-837, 845, 853, 862-874, 891-898, 911, 917, 931-933, 957-964, 991-1013, 1016-1050, 1055-1096, 1105-1165, 1221-1302, 1308-1327, 1353-1362, 1371-1384, 1388 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/c_parser_wrapper.py 177 149 16% 50, 61-192, 196-199, 208-220, 232-337, 341-346, 349-357, 360-365, 375-402, 412-423 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/python_parser.py 630 576 9% 50, 67-176, 179-243, 250-300, 307-323, 333-336, 343-364, 376-573, 587-621, 627-630, 642-683, 698, 701-754, 773-777, 792-825, 828-846, 863-872, 875-878, 885-899, 902-905, 910, 928-989, 992-1075, 1078-1156, 1159-1163, 1180-1204, 1234-1245, 1251-1267, 1272-1281, 1292-1294, 1297, 1313, 1321, 1345-1351 /home/admin/.local/lib/python3.8/site-packages/pandas/io/parsers/readers.py 486 399 18% 473, 478, 483, 502-513, 531-537, 546-583, 640, 697, 754, 811, 884-912, 969, 1026, 1083, 1140, 1213-1242, 1312-1352, 1368-1407, 1410-1412, 1415-1456, 1460-1465, 1472-1620, 1623-1627, 1634-1683, 1686, 1689-1724, 1727-1733, 1736, 1744, 1802-1803, 1808-1840, 1845-1853, 1858-1878, 1936-2008, 2019-2028, 2050-2052, 2074-2106, 2123-2127 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pickle.py 28 15 46% 92-103, 178-204 /home/admin/.local/lib/python3.8/site-packages/pandas/io/pytables.py 2255 1832 19% 118-124, 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5124-5150, 5157-5172, 5194-5230, 5234-5253, 5259-5265, 5271-5289 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/io/sas/sasreader.py 51 29 43% 29, 40, 44, 47, 55, 69, 83, 134-180 /home/admin/.local/lib/python3.8/site-packages/pandas/io/spss.py 22 12 45% 18-20, 54-67 /home/admin/.local/lib/python3.8/site-packages/pandas/io/sql.py 746 610 18% 71-72, 85-90, 96-118, 126-139, 148-165, 178-188, 208-219, 238, 253, 332-353, 368, 383, 464-469, 494, 510, 629-663, 758-769, 804-805, 821-841, 868-886, 889, 892-894, 898-900, 903-914, 928-930, 941-946, 949-986, 992-1030, 1043-1066, 1077-1113, 1117-1145, 1148-1159, 1162-1189, 1207-1244, 1247-1314, 1317-1343, 1352, 1355, 1368, 1382, 1399, 1403, 1407, 1418, 1437, 1442, 1458-1470, 1475-1502, 1526-1544, 1547-1548, 1552-1556, 1560-1563, 1627-1631, 1653-1671, 1738-1765, 1782-1815, 1826-1840, 1908-1933, 1937, 1940-1943, 1946-1956, 1959-1964, 1974-1983, 2001-2005, 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2486-2494, 2509-2567, 2570-2574, 2580-2640, 2650-2677, 2683-2730, 2740-2743, 2759, 2762-2763, 2770-2818, 2821-2822, 2827-2830, 2834-2835, 2839-2840, 2844-2852, 2856-2876, 2880, 2883-2912, 2915, 2919-2920, 2923, 2944-2965, 2972-2974, 3013-3037, 3040-3041, 3072-3092, 3123-3155, 3266-3284, 3289-3291, 3295-3296, 3304-3354, 3362-3384, 3387-3391, 3394-3402, 3405-3407, 3410-3415, 3418-3430, 3434-3462, 3465-3466, 3469-3472, 3475-3476, 3482-3488, 3491-3493, 3501-3504, 3511-3522, 3525-3536, 3659-3684, 3707-3721 /home/admin/.local/lib/python3.8/site-packages/pandas/io/xml.py 241 201 17% 50-54, 162-176, 186, 210-288, 313-390, 407, 421, 432, 442-461, 471-502, 507-520, 527-543, 561-580, 583-607, 612-626, 633-661, 671-676, 696-721, 733-739, 751-758, 801-846, 1116-1118 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_core.py 191 134 30% 30-32, 98-99, 222-223, 474-475, 506-507, 596-597, 791, 802-892, 895-975, 1044, 1136, 1222, 1289, 1351, 1459, 1534, 1583-1589, 1674, 1760-1765, 1786-1831, 1857-1864 /home/admin/.local/lib/python3.8/site-packages/pandas/plotting/_misc.py 73 43 41% 12-16, 40-41, 64-65, 84-85, 159-160, 252-253, 321-322, 387-388, 455-456, 514-515, 549-550, 571-574, 577-578, 581-584, 587-588, 599, 602, 610-615 /home/admin/.local/lib/python3.8/site-packages/pandas/testing.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/__init__.py 4 2 50% 5-11 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/api.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/frequencies.py 307 236 23% 98, 132-175, 184-210, 216, 222, 226, 230, 241-280, 284-285, 289-290, 294, 298, 301, 305-306, 310, 313-342, 345-353, 356-367, 370-381, 384-391, 395-405, 413-427, 432-433, 437, 441-446, 470-506, 525-564, 580-583, 587-589, 593-594, 598-599, 603-604, 608-609 /home/admin/.local/lib/python3.8/site-packages/pandas/tseries/offsets.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pandas/util/_decorators.py 135 79 41% 56-94, 164-214, 243-252, 257-260, 291-337, 368, 436, 448-449, 491 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_exceptions.py 48 36 25% 16-27, 36-51, 75-89 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_print_versions.py 48 34 29% 24-27, 34-36, 56-90, 108-134 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_str_methods.py 12 1 92% 23 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_tester.py 18 11 39% 25-35 /home/admin/.local/lib/python3.8/site-packages/pandas/util/_validators.py 122 87 29% 33-41, 55-79, 117-123, 132-136, 161-163, 206-221, 259, 285-302, 326-336, 341, 346, 357, 377-390, 409-424, 435-442, 446-448 /home/admin/.local/lib/python3.8/site-packages/pandas/util/version/__init__.py 270 129 52% 28, 31, 34, 37, 40, 43, 46, 49, 52, 60, 63, 66, 69, 72, 75, 78, 81, 84, 123-126, 139, 146, 151-154, 157-160, 164, 169-172, 175-178, 183-186, 193, 196, 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 255-268, 276-294, 338, 363, 366-391, 395-396, 400-401, 405-406, 410, 418-421, 425, 429-438, 442, 446, 450, 454, 458, 462, 471-489, 493-495, 508, 537, 543, 550, 557, 570 /home/admin/.local/lib/python3.8/site-packages/psutil/__init__.py 950 691 27% 37-38, 127-128, 131-132, 135-136, 139-140, 143-180, 248-259, 271-281, 290-292, 298-304, 346, 349-393, 396-411, 421-423, 426, 429-431, 436, 467-505, 518-549, 556-568, 574-579, 586-601, 617-618, 628-647, 654-686, 690, 694-697, 703-715, 722-724, 728, 732-737, 746, 752, 758, 764, 776, 793-798, 813-816, 829-837, 850, 858, 862-866, 872, 876, 886, 915-956, 993-1048, 1059, 1070, 1074, 1090, 1102-1119, 1133-1147, 1154, 1178, 1184-1202, 1211-1212, 1222-1223, 1233-1234, 1244-1245, 1254-1255, 1273-1275, 1322-1323, 1326, 1329-1331, 1334-1347, 1350-1356, 1360-1364, 1383-1385, 1393-1403, 1431-1482, 1521-1571, 1593-1599, 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873-908, 913-946, 949-977, 985, 992-1022, 1027-1043, 1058-1146, 1151-1182, 1203-1290, 1303-1322, 1332-1405, 1415-1428, 1434-1441, 1452, 1459-1484, 1491-1506, 1515-1526, 1536-1539, 1545, 1558-1580, 1589-1590, 1595-1597, 1600-1602, 1605-1607, 1611-1615, 1618-1632, 1636-1657, 1661-1663, 1667-1672, 1678-1704, 1709-1715, 1720, 1724, 1728-1735, 1750-1753, 1766-1788, 1801-1858, 1862-1869, 1874-1883, 1890-1891, 1895-1919, 1928, 1932, 1939, 1944-1949, 1953-1968, 1975-1978, 1982-1988, 1997-2017, 2021-2025, 2029-2069, 2073-2075, 2079, 2083, 2087-2089, 2093-2095 /home/admin/.local/lib/python3.8/site-packages/psutil/_psposix.py 89 67 25% 30-47, 61-110, 120-158, 167-175 /home/admin/.local/lib/python3.8/site-packages/pyarrow/__init__.py 170 121 29% 41-59, 80-93, 97-102, 106-116, 123-157, 312-315, 343, 347, 351-356, 360-367, 375, 393-416, 426-466 /home/admin/.local/lib/python3.8/site-packages/pyarrow/_compute_docstrings.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/_generated_version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/compute.py 203 92 55% 114, 137-138, 175-179, 208-211, 215-232, 238-245, 248-262, 391-403, 425-441, 484-485, 539-544, 584-591, 631-638, 663-664, 700-713, 730 /home/admin/.local/lib/python3.8/site-packages/pyarrow/filesystem.py 225 139 38% 54-55, 66, 79, 94-105, 108, 118, 124, 130, 143, 156, 167, 178, 189, 196, 225-228, 235, 239, 260-261, 265-266, 270-274, 278-279, 283-284, 288, 292-293, 300-301, 305, 311-312, 321-325, 329, 333, 341, 345-346, 350-351, 355-359, 366-367, 370-371, 377-378, 385-393, 397-402, 411-433, 437-440, 444-459, 467-511 /home/admin/.local/lib/python3.8/site-packages/pyarrow/hdfs.py 82 52 37% 42-49, 52, 59, 63, 67, 71, 86, 90, 94, 111, 126-131, 135-149, 153-165, 169-172, 176-185, 223-227, 235-240 /home/admin/.local/lib/python3.8/site-packages/pyarrow/ipc.py 61 36 41% 51-52, 84-85, 109-110, 121-122, 126-141, 146-150, 154, 190, 195, 234, 259-264, 282-285 /home/admin/.local/lib/python3.8/site-packages/pyarrow/types.py 155 47 70% 56, 61, 66, 71, 76, 81, 86, 91, 96, 101, 106, 111, 116, 121, 126, 131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191, 196, 201, 206, 211, 223, 228, 233, 238, 243, 248, 253, 258, 263, 268, 273, 278, 283, 288, 293 /home/admin/.local/lib/python3.8/site-packages/pyarrow/util.py 96 60 38% 64, 101-106, 114-123, 127-131, 135, 142-151, 158, 177-194, 198-202, 206-207, 213-230 /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/pyarrow/vendored/docscrape.py 473 282 40% 24, 46, 62, 82, 101, 104, 109-112, 155-157, 164, 169, 172, 183, 187-188, 222, 224, 238-241, 297-341, 349-360, 365, 373-376, 395-396, 398-399, 406, 412-420, 424-428, 431-442, 447, 450, 453-455, 458-460, 463-465, 468-481, 484-489, 492-521, 524-538, 541-555, 565-574, 577-582, 585-599, 604-607, 616-666, 670-672, 680-682, 689-693, 697-716 /home/admin/.local/lib/python3.8/site-packages/pycparser/__init__.py 25 18 28% 32-48, 82-90 /home/admin/.local/lib/python3.8/site-packages/pycparser/ast_transforms.py 21 18 14% 64-96, 103-105 /home/admin/.local/lib/python3.8/site-packages/pycparser/c_ast.py 782 446 43% 25-28, 37-51, 56, 80-100, 149-158, 164-165, 176-179, 182-185, 192-194, 197-200, 203-206, 213-216, 219-222, 225-228, 235-238, 241-244, 247-250, 257, 260, 263-264, 271-273, 276-280, 283-286, 293-295, 298-301, 304-307, 314-315, 318-321, 324-325, 332-334, 337-340, 343-346, 358-359, 362-363, 370, 373, 376-377, 394-398, 401-406, 413-414, 417-420, 423-424, 431-432, 435-438, 441-442, 449-451, 454-457, 460-463, 470, 473, 476-477, 484, 487, 490-491, 498-500, 503-505, 508-509, 516-518, 521-523, 526-527, 534-535, 538-541, 544-545, 552-553, 556-559, 562-563, 574-577, 580-581, 588-592, 595-600, 603-610, 617-619, 622-625, 628-631, 643-646, 649-652, 659-662, 665-670, 673-678, 685-686, 689-690, 693-694, 701-702, 705-706, 709-710, 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339-340, 360-361, 368-396, 399-407, 411, 416, 419-422, 440, 450-454, 463-469, 494-523, 546, 578-581, 587-589, 592-594, 597-599, 605-612, 622-630, 637-638, 642-644, 647-649, 651-653, 655-657, 679-681, 693-694, 696-699, 708, 711-712, 717-718, 730-822, 833-856, 867, 881-882, 896, 902-903, 909-910, 920-1047, 1056-1080, 1092 /home/admin/.local/lib/python3.8/site-packages/pycparser/ply/yacc.py 1916 1562 18% 97, 114, 119, 122, 129, 132, 140-146, 150-156, 176-177, 180-181, 184-185, 190-198, 219, 222, 242, 246, 252, 264-266, 272-274, 277, 295, 298-303, 321, 325-327, 329, 350-683, 697-989, 1015-1016, 1030, 1063, 1087-1088, 1123-1135, 1157-1168, 1176-1271, 1312-1339, 1342, 1345, 1348, 1351, 1354, 1358-1370, 1374-1375, 1392, 1395, 1429-1437, 1440-1444, 1447, 1455-1460, 1475-1507, 1511, 1514, 1525-1530, 1551-1624, 1634-1640, 1652-1662, 1673-1727, 1737-1745, 1754-1759, 1769-1774, 1786-1791, 1804-1829, 1837-1864, 1875-1914, 1933-1958, 1980, 1986, 1999-2023, 2056-2064, 2067-2087, 2101-2133, 2138-2154, 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383-410, 416-423, 430-434, 441-457, 467-477, 484-486, 495-496, 505-511, 518-523, 531-533 /home/admin/.local/lib/python3.8/site-packages/pydrive/drive.py 15 5 67% 16-17, 28, 39, 47 /home/admin/.local/lib/python3.8/site-packages/pydrive/files.py 267 199 25% 41-43, 55, 63-72, 95-120, 133-142, 155-157, 169-173, 190-194, 207-213, 228-244, 252-270, 279-285, 292, 300, 309, 322-331, 341-342, 352, 362-375, 384-395, 405-416, 427-435, 446-460, 471-481, 490-492, 503-506, 518-531, 543-571, 583-609 /home/admin/.local/lib/python3.8/site-packages/pydrive/settings.py 46 34 26% 5-6, 141-146, 156, 169-171, 186-213 /home/admin/.local/lib/python3.8/site-packages/pygit2/__init__.py 92 43 53% 123-162, 203-225 /home/admin/.local/lib/python3.8/site-packages/pygit2/_build.py 18 12 33% 46-53, 58-67 /home/admin/.local/lib/python3.8/site-packages/pygit2/blame.py 69 36 48% 33-36, 44-47, 52, 58, 63, 68, 72, 77, 82, 86, 91-95, 102-105, 108, 111, 114-118, 130-137, 140 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/home/admin/.local/lib/python3.8/site-packages/python_http_client/client.py 106 27 75% 11-15, 52, 59-62, 126, 129-130, 135, 145, 177-184, 208-216, 243, 246, 253, 293, 296 /home/admin/.local/lib/python3.8/site-packages/python_http_client/exceptions.py 46 16 65% 8-17, 20, 30, 93-97 /home/admin/.local/lib/python3.8/site-packages/pytz/__init__.py 198 125 37% 56-75, 87-108, 113-124, 167-190, 195, 204-206, 226-228, 231, 234, 237, 240, 244-246, 250-254, 257, 260, 295, 307, 347, 350-366, 379-390, 403-406, 409, 412, 415, 418, 421, 425-427, 431-435, 491-502, 509-512, 516 /home/admin/.local/lib/python3.8/site-packages/pytz/exceptions.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/pytz/lazy.py 100 59 41% 4-8, 21-28, 31-38, 41-48, 51-58, 61-68, 87, 98-106, 142, 151-160 /home/admin/.local/lib/python3.8/site-packages/pytz/tzfile.py 76 66 13% 21, 25-123, 126-133 /home/admin/.local/lib/python3.8/site-packages/pytz/tzinfo.py 178 126 29% 7-8, 34-41, 49-58, 66, 76, 87-89, 97, 105, 113, 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/home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_macroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_pdfdoc.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_standard.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_winansi.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_enc_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courier.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courierboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_courieroblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helvetica.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticabold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaboldoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_helveticaoblique.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_symbol.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbold.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesbolditalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesitalic.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_timesroman.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/_fontdata_widths_zapfdingbats.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/reportlab/pdfbase/pdfdoc.py 1657 716 57% 32-35, 78, 89, 140, 165, 188-189, 194, 203, 206-213, 219, 223, 229-230, 236, 251-254, 273, 276-277, 283, 301, 304, 307-308, 311-314, 317-335, 342, 350, 359, 366, 373, 380, 385, 388-395, 410, 432-436, 444, 462, 468-469, 475-487, 516, 519, 526-529, 550, 554-555, 558, 565-572, 579-580, 593-595, 604-621, 627-633, 637-638, 642-643, 645, 647, 650, 653, 662, 676, 678, 680, 685-687, 701, 705-708, 714-717, 720, 725-726, 729-734, 766-769, 780, 783, 805, 832-839, 856, 861-864, 893-894, 913, 948, 950, 972, 983, 1041, 1044, 1047-1048, 1051-1052, 1070, 1116, 1127, 1134-1135, 1139, 1154, 1159, 1179, 1195, 1198-1209, 1274-1278, 1281-1283, 1287-1296, 1312, 1318-1327, 1362-1395, 1398, 1402-1408, 1411-1412, 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/home/admin/.local/lib/python3.8/site-packages/requests/__version__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/_internal_utils.py 21 3 86% 33, 49-50 /home/admin/.local/lib/python3.8/site-packages/requests/adapters.py 194 82 58% 61, 93, 97, 145, 158, 163-169, 211-235, 255, 261, 271-275, 278-290, 318, 343-351, 368, 390-391, 395, 426-432, 455-456, 472-476, 481, 500-536 /home/admin/.local/lib/python3.8/site-packages/requests/api.py 19 6 68% 85, 99-100, 130, 145, 157 /home/admin/.local/lib/python3.8/site-packages/requests/auth.py 173 141 18% 35-66, 73, 80-81, 84, 92, 95-96, 103-104, 111-114, 118-124, 131-234, 238-239, 250-284, 288-304, 307, 315 /home/admin/.local/lib/python3.8/site-packages/requests/certs.py 4 1 75% 17 /home/admin/.local/lib/python3.8/site-packages/requests/compat.py 30 5 83% 12-13, 36-37, 42 /home/admin/.local/lib/python3.8/site-packages/requests/cookies.py 239 149 38% 19-20, 41, 44, 47, 52-58, 70, 73, 76, 80, 85, 92, 96, 100, 121, 132, 156-167, 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4 76% 5-10 /home/admin/.local/lib/python3.8/site-packages/requests/sessions.py 268 118 56% 56, 86, 100-103, 116-124, 129-157, 178-281, 288-301, 315-332, 338-354, 601-602, 612-613, 623-624, 637, 649, 661, 671, 684, 689, 716-717, 732-735, 763, 794, 810, 813-814, 817-818, 833 /home/admin/.local/lib/python3.8/site-packages/requests/status_codes.py 14 0 100% /home/admin/.local/lib/python3.8/site-packages/requests/structures.py 39 11 72% 55, 65, 68-73, 77, 80, 91, 96, 99 /home/admin/.local/lib/python3.8/site-packages/requests/utils.py 485 315 35% 76-121, 127-130, 141, 144-157, 170-191, 204, 216-220, 223-224, 230-253, 274-297, 303-310, 331-337, 358, 361, 393-398, 424-433, 445-459, 469-474, 485, 493-506, 548, 556, 566-577, 582-587, 602-626, 643-655, 674-678, 689-693, 703-704, 711-715, 724-739, 752-753, 758-761, 784, 789-809, 815-816, 819, 831, 845, 856-857, 873-886, 920-946, 962-984, 993-1013, 1038-1040, 1044-1056, 1068-1076, 1083-1094 /home/admin/.local/lib/python3.8/site-packages/seaborn/__init__.py 16 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/_core.py 638 564 12% 48, 52-54, 58, 62-65, 90-138, 142-159, 163-174, 180-211, 215-254, 273-314, 318-325, 329-338, 342-399, 403-478, 498-546, 550-554, 558-577, 604-615, 621-627, 632, 647-653, 657-681, 706-821, 850-935, 963-1016, 1021-1058, 1062-1073, 1091-1161, 1165-1178, 1186-1191, 1218-1273, 1304-1348, 1369-1399, 1418-1445, 1467-1484 /home/admin/.local/lib/python3.8/site-packages/seaborn/_decorators.py 31 7 77% 32-46 /home/admin/.local/lib/python3.8/site-packages/seaborn/_docstrings.py 36 1 97% 30 /home/admin/.local/lib/python3.8/site-packages/seaborn/_statistics.py 196 168 14% 69-79, 83-87, 91-96, 100-114, 118-126, 130-140, 144-158, 162-182, 186-189, 232-241, 245-259, 263-312, 316-342, 346-366, 370-373, 389-391, 395, 399-413, 417-426 /home/admin/.local/lib/python3.8/site-packages/seaborn/algorithms.py 66 60 9% 36-86, 91-105, 114-129 /home/admin/.local/lib/python3.8/site-packages/seaborn/axisgrid.py 1011 915 9% 38-43, 47-50, 54-56, 60-64, 95-176, 180-183, 187-196, 200-226, 231-234, 309-474, 581-606, 632-688, 720-755, 759-763, 768-779, 783-785, 791-799, 803-804, 808-815, 819-826, 830-837, 841-853, 857-865, 890-957, 964, 969, 974-980, 991, 998-1011, 1016-1023, 1028-1035, 1040-1052, 1057-1069, 1145-1250, 1263-1267, 1280-1282, 1295-1297, 1310-1321, 1335-1406, 1411-1447, 1454-1469, 1473-1508, 1512-1553, 1557-1562, 1566-1570, 1591-1669, 1673-1676, 1700-1702, 1724-1738, 1761-1799, 1820-1822, 1830-1831, 1962-2049, 2064-2214 /home/admin/.local/lib/python3.8/site-packages/seaborn/categorical.py 1249 1116 11% 44-240, 245-265, 269-324, 329-337, 342-346, 350-388, 392-397, 406-415, 419-476, 481-505, 509-512, 522-546, 551-671, 675-686, 690-692, 702-714, 718-724, 728-747, 752-757, 761-935, 939-946, 954-979, 987-995, 1001-1010, 1015-1016, 1021-1039, 1043-1046, 1057-1082, 1086-1088, 1099-1112, 1116-1156, 1160-1164, 1172-1177, 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2395-2403, 2543-2661 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/seaborn/external/docscrape.py 455 272 40% 43, 64, 80, 100, 119, 122, 127-130, 173-175, 182, 187, 190, 201, 236, 238, 250-253, 308-350, 358-369, 374, 382-385, 391, 404-405, 407-408, 415, 421-429, 432-443, 448, 451-454, 457-460, 463-466, 469-472, 475-488, 491-496, 499-528, 531-546, 549-563, 567-571, 580, 585-609, 612-617, 620-634, 643-691, 695-697, 705-707, 714-718 /home/admin/.local/lib/python3.8/site-packages/seaborn/external/husl.py 194 151 22% 32, 36, 40, 44, 48, 52, 56, 60, 64, 68, 72-91, 95-114, 118-119, 123, 127-130, 134-137, 141-144, 148-153, 157-175, 179-184, 188-189, 193-194, 198-199, 203-219, 223-235, 239-247, 251-257, 261-271, 275-285, 289-299, 303-313 /home/admin/.local/lib/python3.8/site-packages/seaborn/matrix.py 548 491 10% 25-28, 33-36, 41-49, 61-86, 98-182, 189-239, 243-255, 259-268, 272-284, 289-343, 535-549, 563-616, 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/home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/__init__.py 14 6 57% 19, 30, 46-50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/eventwebhook/eventwebhook_header.py 5 1 80% 10 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/__init__.py 63 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/amp_html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/asm.py 33 20 39% 16-23, 31, 40-43, 52, 62-65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/attachment.py 75 47 37% 43-62, 70, 79-82, 90, 99-102, 110, 119-122, 137, 162-165, 176, 191-194, 203-218 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/batch_id.py 15 7 53% 14-17, 25, 34, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings.py 27 16 41% 16-23, 31, 40, 48, 57, 66-72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bcc_settings_email.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_bounce_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_list_management.py 16 9 44% 16-19, 27, 36, 45-48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_spam_management.py 16 9 44% 15-18, 26, 35, 44-47 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/bypass_unsubscribe_management.py 16 9 44% 17-20, 28, 37, 46-49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/category.py 13 6 54% 10-13, 21, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/cc_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/click_tracking.py 27 16 41% 12-19, 27, 36, 45, 56, 65-71 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/content_id.py 13 6 54% 13-16, 27, 41, 50 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/custom_arg.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/disposition.py 13 6 54% 21-24, 39, 63, 72 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/dynamic_template_data.py 24 12 50% 16-22, 30, 39, 47, 57, 64, 73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/email.py 79 13 84% 50-54, 78, 137, 154, 171, 189, 202-203, 209, 224 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/exceptions.py 22 10 55% 24-31, 39, 48, 56, 65 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_content.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_name.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/file_type.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_html.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_settings.py 38 23 39% 14-25, 33, 42, 50, 59, 67, 76, 85-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/footer_text.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/from_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ganalytics.py 63 35 44% 26-38, 48-49, 57, 66, 75, 86, 94, 103, 111, 120, 128, 137, 145, 154, 163-176 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/group_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/groups_to_display.py 15 8 47% 13-16, 25, 37-39, 48 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/header.py 34 19 44% 21-30, 38, 47, 55, 64, 72, 82, 91-94 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/html_content.py 25 14 44% 14-18, 26, 34, 43-44, 53-59 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/ip_pool_name.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail.py 470 241 49% 72, 78, 88, 151-167, 171, 177-178, 183-187, 213, 229-241, 262-265, 273, 280, 296-308, 324-330, 335, 356-368, 388-394, 416-431, 445, 454-458, 466-490, 494, 503-507, 515-535, 547, 557-561, 569-592, 612-629, 633, 642-653, 674, 692-696, 708, 717-721, 737-748, 752, 768, 777-781, 789, 806-809, 821, 829-833, 841, 853, 861-865, 872, 889, 906, 923, 940, 957, 997-1013 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mail_settings.py 93 58 38% 38-69, 77, 86, 94, 103, 111, 120, 128, 137, 145, 154, 162, 171, 179, 188, 196, 206, 215-243 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/mime_type.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking.py 27 16 41% 16-23, 31, 40, 50, 65, 74-80 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/open_tracking_substitution_tag.py 13 6 54% 12-15, 26, 39, 49 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/personalization.py 130 38 71% 23-29, 55, 63-67, 70, 73-76, 90, 98, 110, 118, 133, 145, 152, 164, 171-174, 186, 193, 207, 220-223, 242, 247-250 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/plain_text_content.py 25 14 44% 15-19, 27, 35, 44-45, 54-60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/reply_to.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/sandbox_mode.py 16 9 44% 12-15, 23, 32, 41-44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/section.py 25 14 44% 12-18, 26, 35, 43, 52, 61-64 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/send_at.py 24 12 50% 22-28, 36, 45, 53, 63, 70, 79 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_check.py 44 27 39% 18-27, 35, 44, 54, 68-71, 80, 91-94, 103-112 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_threshold.py 13 6 54% 15-18, 29, 44, 53 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/spam_url.py 13 6 54% 12-15, 24, 35, 44 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subject.py 23 4 83% 18, 43, 53, 60 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_html.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_substitution_tag.py 13 6 54% 18-21, 32, 48, 58 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_text.py 13 6 54% 12-15, 24, 36, 45 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/subscription_tracking.py 49 30 39% 21-33, 41, 50, 59, 71, 80, 92, 103, 120, 129-142 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/substitution.py 34 19 44% 17-26, 34, 43, 51, 60, 68, 78, 87-90 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/template_id.py 13 6 54% 10-13, 21, 30, 39 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/to_email.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/tracking_settings.py 49 30 39% 30-45, 53, 63, 71, 81, 89, 98, 106, 115, 124-134 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_campaign.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_content.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_medium.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_source.py 13 6 54% 11-14, 23, 34, 43 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/utm_term.py 13 6 54% 11-14, 22, 31, 40 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/mail/validators.py 27 10 63% 23-24, 46-55, 69 /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/helpers/stats/stats.py 166 108 35% 12-22, 29, 38-53, 61, 70, 78, 87, 95, 104, 112, 121, 129, 138, 146, 155, 163, 172, 187-194, 202-220, 228, 236-238, 253-260, 268-286, 294, 302-304, 317-319, 327, 336, 344, 357-359, 367, 376, 384 /home/admin/.local/lib/python3.8/site-packages/sendgrid/sendgrid.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sendgrid/twilio_email.py 9 4 56% 63-73 /home/admin/.local/lib/python3.8/site-packages/sendgrid/version.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/__check_build/__init__.py 18 12 33% 19-31, 45-46 /home/admin/.local/lib/python3.8/site-packages/sklearn/__init__.py 29 9 69% 69, 103-112 /home/admin/.local/lib/python3.8/site-packages/sklearn/_config.py 21 13 38% 75-82, 144-150 /home/admin/.local/lib/python3.8/site-packages/sklearn/_distributor_init.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/_loss/glm_distribution.py 86 54 37% 59-66, 132, 156, 175, 204, 208, 215-235, 246, 272-323, 329, 335, 341, 347 /home/admin/.local/lib/python3.8/site-packages/sklearn/base.py 259 142 45% 54-88, 108-138, 159, 169, 197-198, 221-244, 265-291, 304, 310, 322, 325-333, 362, 365, 413-419, 424-434, 449-453, 460, 464-467, 499-500, 503, 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303-315, 320-321, 437-441, 460-461, 464-518, 529-534, 555-561, 564-568, 588-595, 614-617, 623-659 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_dbscan.py 52 37 29% 141-145, 276-283, 310-361, 389-390 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_feature_agglomeration.py 19 12 37% 38-51, 70-73 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_kmeans.py 526 454 14% 85-144, 152-158, 289-298, 354-428, 484-541, 579-601, 771-781, 785-844, 848-853, 858-861, 869-887, 924-951, 979-1052, 1077, 1105, 1124-1127, 1131, 1154-1160, 1183-1189, 1193, 1264-1337, 1346-1403, 1571-1579, 1585, 1591, 1597, 1600-1633, 1660-1781, 1805-1813, 1835-1891, 1914-1917, 1920, 1988-2019 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_mean_shift.py 116 94 19% 68-86, 92-109, 186-191, 223-240, 358-364, 377-450, 465-468 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_optics.py 218 193 11% 210-222, 246-287, 291-298, 327-338, 448-503, 509-538, 571-578, 631-646, 690-709, 716-722, 736-744, 790-898, 920-927 /home/admin/.local/lib/python3.8/site-packages/sklearn/cluster/_spectral.py 120 96 20% 76-158, 260-284, 462-476, 499-544, 568, 571, 580 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/__init__.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_base.py 47 28 40% 37-44, 57-77, 131, 154-158 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_dict_learning.py 359 309 14% 27-28, 113-191, 297-354, 394-435, 547-632, 766-891, 899-905, 910-926, 945-946, 1066-1071, 1089, 1096, 1115, 1118, 1122, 1126, 1304-1318, 1336-1358, 1546-1560, 1578-1599, 1623-1650 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_factor_analysis.py 139 119 14% 155-167, 183-264, 282-294, 306-310, 320-339, 354-362, 379, 384-390, 396-414 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_fastica.py 181 154 15% 49-50, 57-60, 69-95, 104-122, 128-136, 140-143, 147, 272-301, 399-411, 431-540, 557, 574-575, 593-600, 617-624 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_incremental_pca.py 88 76 14% 170-173, 191-216, 237-319, 350-358 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_kernel_pca.py 106 80 25% 151-168, 176, 179-185, 192-256, 259-267, 283-295, 310-318, 331-344, 361-369, 372 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_lda.py 238 208 13% 75-132, 303-318, 322-335, 341-362, 396-426, 456-476, 479, 491-494, 510-537, 556-609, 624-640, 658-664, 692-740, 757-763, 787-813, 837 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_nmf.py 422 383 9% 38, 51, 55-61, 91-167, 172-187, 192-203, 207-233, 238-248, 311-401, 413-432, 508-538, 544-633, 638-715, 789-850, 1022-1093, 1268-1279, 1282, 1307-1326, 1342-1343, 1358-1374, 1391-1392 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_pca.py 164 92 44% 59-97, 105-109, 376-386, 394, 402-405, 414, 416, 424-427, 435-436, 439, 445, 468, 476-477, 484, 500-564, 583-592, 613, 616 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_sparse_pca.py 68 52 24% 116-126, 144-175, 198-206, 309-316, 334-362 /home/admin/.local/lib/python3.8/site-packages/sklearn/decomposition/_truncated_svd.py 58 39 33% 125-129, 146-147, 164-208, 223-225, 242-243, 246 /home/admin/.local/lib/python3.8/site-packages/sklearn/exceptions.py 15 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/isotonic.py 109 84 23% 56-76, 117-131, 222-225, 228-231, 237-247, 252-295, 325-342, 361-385, 400, 404-407, 414-416, 419 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/__init__.py 17 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_base.py 207 160 23% 81-101, 124-179, 197-207, 218-221, 238, 245-249, 252, 282-293, 309-314, 323-330, 353-357, 385-388, 485-489, 514-575, 588-642 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_bayes.py 203 180 11% 162-174, 197-300, 324-332, 343-356, 361-386, 515-526, 546-633, 641-650, 656-661, 685-694 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_coordinate_descent.py 492 409 17% 58-75, 124-168, 311, 440-551, 706-717, 751-874, 879, 893-898, 1031, 1085-1146, 1157-1171, 1200-1356, 1518, 1526, 1529, 1532, 1725-1740, 1743, 1746, 1749, 1880-1889, 1913-1958, 1961, 2078-2087, 2263-2276, 2279, 2282, 2285, 2444, 2452, 2455, 2458 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/glm.py 158 122 23% 32-35, 40-48, 133-141, 161-298, 313-317, 333-335, 371-376, 380-388, 458, 465, 469-470, 540, 547, 551-552, 654, 664-666, 670-673 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_glm/link.py 40 13 68% 68, 71, 74, 77, 84, 87, 90, 93, 100, 103, 106, 109-110 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_huber.py 88 74 16% 52-122, 229-234, 255-307 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_least_angle.py 435 384 12% 166-171, 301, 442-798, 917-926, 930-936, 940-994, 1017-1035, 1177-1188, 1195-1197, 1283-1310, 1438-1442, 1449, 1467-1529, 1685-1695, 1823-1832, 1835, 1858-1904 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py 546 500 8% 75-82, 114-133, 162-169, 202-246, 286-302, 343-355, 396-428, 432-458, 462-475, 632-819, 957-1009, 1261-1275, 1306-1435, 1463-1478, 1499, 1751-1767, 1789-2062, 2085-2088, 2091 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_omp.py 273 245 10% 72-138, 194-264, 349-408, 490-544, 632-636, 655-687, 735-764, 870-876, 894-919 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_passive_aggressive.py 34 20 41% 173-191, 216-228, 254-256, 401-418, 435-437, 464-466 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_perceptron.py 6 1 83% 164 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ransac.py 153 133 13% 47-54, 215-226, 256-464, 480-482, 502-504, 507 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_ridge.py 614 520 15% 41-114, 118-132, 137-156, 161-217, 221-228, 232-235, 366, 385-518, 527-534, 539-600, 737, 762, 895-899, 924-946, 950, 954-966, 982-984, 995-999, 1002-1003, 1008, 1014, 1025-1029, 1032-1040, 1043-1050, 1057, 1060, 1070, 1073, 1122-1130, 1135, 1140-1143, 1176-1192, 1223-1236, 1261-1276, 1281-1290, 1297-1313, 1319-1338, 1347-1355, 1370-1385, 1394-1397, 1402-1412, 1421-1433, 1454-1581, 1590-1597, 1627-1665, 1917-1920, 1943-1959, 1963, 1966 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_sag.py 75 63 16% 67-85, 234-344 /home/admin/.local/lib/python3.8/site-packages/sklearn/linear_model/_stochastic_gradient.py 446 345 23% 56-62, 65-68, 80-102, 117-119, 127-156, 160-168, 171-174, 178-182, 187-241, 258-281, 285-288, 298, 307, 315, 323, 331-355, 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89-95, 120-141, 221-360, 463-469, 472, 481, 503-536, 560-579, 601-602 /home/admin/.local/lib/python3.8/site-packages/sklearn/manifold/_t_sne.py 258 224 13% 58-64, 94-121, 163-193, 255-272, 345-401, 453-477, 664-679, 684-841, 854-910, 932-934, 950-951 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/__init__.py 78 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_base.py 78 71 9% 67-131, 175-202, 234-251 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py 511 452 12% 48-52, 83-128, 132-137, 202-210, 296-355, 456-557, 618-639, 758-785, 852-875, 935-946, 1068, 1192-1200, 1214-1247, 1251-1261, 1269-1299, 1458-1540, 1653-1660, 1771-1778, 1846-1858, 1966-2060, 2135-2152, 2225-2281, 2365-2403, 2477-2506 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/base.py 37 33 11% 26-45, 81-114 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/confusion_matrix.py 61 47 23% 71-72, 107-162, 255-272 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/det_curve.py 44 36 18% 65-68, 88-129, 210-229 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/precision_recall_curve.py 47 35 26% 77-81, 107-140, 203-225 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_plot/roc_curve.py 47 35 26% 73-77, 100-132, 210-230 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_ranking.py 336 292 13% 83-106, 199-224, 294-317, 326-349, 522-547, 595-648, 688-730, 811-823, 913-956, 1005-1046, 1090-1106, 1149-1191, 1238-1250, 1290-1299, 1303-1307, 1407-1411, 1458-1466, 1564-1569, 1646-1717 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_regression.py 168 137 18% 88-122, 182-194, 257-271, 335-351, 408-416, 477-492, 552-584, 676-723, 753-756, 808-821, 857, 896 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/_scorer.py 226 132 42% 52-60, 77, 81-92, 107-122, 133-134, 155-166, 169-171, 199, 204, 236-242, 276-288, 291, 326-362, 365, 383-392, 397, 426-459, 485-530, 614 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/__init__.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_bicluster.py 32 22 31% 12-17, 22-28, 38-45, 80-86 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_supervised.py 170 139 18% 43-69, 74-83, 127-149, 214-229, 289-299, 383-389, 453-473, 542, 611, 710, 768-798, 889-919, 998-1020, 1091-1100, 1115-1123 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/cluster/_unsupervised.py 93 76 18% 33-34, 109-117, 135-149, 214-248, 281-298, 339-363 /home/admin/.local/lib/python3.8/site-packages/sklearn/metrics/pairwise.py 412 339 18% 45-61, 135-164, 194-198, 272-323, 399-439, 451-508, 512-514, 587-601, 670-673, 722-723, 782-804, 833-841, 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195-221, 239-275, 414-421, 441-487, 504-529, 548-616 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_graph.py 60 35 42% 16-21, 29-36, 106-113, 187-194, 307-311, 327, 345-347, 371, 374, 490-494, 510, 528-529, 553, 556 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_kde.py 86 66 23% 101-119, 124-139, 164-179, 197-211, 233, 256-283, 286 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_lof.py 80 54 32% 184-190, 218-223, 246, 265-299, 320-327, 346-356, 384-393, 421, 450-458, 486-498, 521-526 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nca.py 157 129 18% 169-176, 197-244, 265-268, 304-377, 400-443, 453-456, 483-524, 527 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_nearest_centroid.py 70 54 23% 91-92, 107-181, 202-205 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_regression.py 62 40 35% 152-157, 161, 170, 190, 206-229, 352-358, 378, 395-426 /home/admin/.local/lib/python3.8/site-packages/sklearn/neighbors/_unsupervised.py 10 2 80% 118, 142 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/__init__.py 28 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_data.py 810 681 16% 71-80, 161-217, 323-325, 335-341, 362-363, 386-417, 432-441, 456-463, 466, 545-561, 683-685, 695-699, 726-727, 762-860, 877-897, 914-937, 940, 1007, 1017-1020, 1040-1041, 1065-1085, 1100-1110, 1125-1134, 1137, 1198-1215, 1319-1323, 1344-1387, 1402-1416, 1431-1444, 1447, 1536-1555, 1632-1635, 1639-1641, 1646-1651, 1668-1681, 1701-1708, 1739-1837, 1891-1937, 2001-2002, 2023-2024, 2043-2045, 2048, 2083-2098, 2157-2158, 2179-2180, 2200-2205, 2208, 2253, 2272-2282, 2299-2310, 2313, 2321, 2350-2378, 2484-2489, 2499-2519, 2531-2568, 2589-2625, 2630-2694, 2699-2717, 2737-2750, 2768-2771, 2789-2793, 2796, 2920-2931, 3020-3022, 3043-3044, 3047, 3050-3077, 3092-3106, 3139-3152, 3158-3163, 3169-3185, 3192-3207, 3217-3219, 3228-3243, 3267-3290, 3293, 3394-3395 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_discretization.py 117 102 13% 131-134, 153-237, 242-271, 288-318, 337-353 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_encoders.py 272 241 11% 42-67, 70-74, 77-110, 113-156, 159, 318-322, 325-333, 339-397, 416-420, 442-443, 459-505, 524-600, 617-635, 721-724, 743-771, 787-793, 809-844 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_function_transformer.py 41 26 37% 11, 91-97, 100-102, 106-109, 128-132, 147, 162, 166-171, 174 /home/admin/.local/lib/python3.8/site-packages/sklearn/preprocessing/_label.py 274 229 16% 100-102, 116-118, 132-138, 152-163, 166, 262-274, 289-298, 321, 343-350, 387-403, 406, 471-569, 577-613, 619-657, 725-726, 742-754, 773-798, 816-824, 827-831, 847-864, 881-898, 902 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_base.py 362 291 20% 39-60, 81-104, 108, 117, 152-240, 249-250, 253-255, 262-287, 291-323, 342-344, 347-361, 370-378, 393-400, 417-430, 433-440, 449-457, 471-496, 500-514, 517, 521-532, 542-544, 552-564, 592-595, 614-625, 632-636, 666-667, 670-676, 706-707, 710, 713-727, 730-738, 752-764, 768, 772, 791-830, 930-995 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_bounds.py 21 14 33% 54-74 /home/admin/.local/lib/python3.8/site-packages/sklearn/svm/_classes.py 124 65 48% 187-198, 224-246, 249, 382-391, 417-432, 435, 657, 667, 877, 887, 1042, 1054, 1062, 1065, 1211, 1218, 1346, 1376-1379, 1396-1397, 1412, 1431-1432, 1440, 1448, 1451 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_classes.py 291 224 23% 103-115, 128-129, 139-140, 145-397, 401-411, 436-463, 489-491, 515-516, 520-539, 576-578, 598-600, 846, 898-903, 929-951, 970-979, 1197, 1247-1252, 1271-1277, 1511, 1732 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_export.py 377 339 10% 44-70, 75, 181-194, 203-214, 218-237, 241-262, 266-366, 376-406, 412-427, 431-436, 439-463, 466-524, 534-560, 565-574, 577-625, 628-662, 769-795, 802-815, 876-972 /home/admin/.local/lib/python3.8/site-packages/sklearn/tree/_reingold_tilford.py 131 110 16% 9-22, 25, 28, 31-38, 41-44, 48, 51, 54-56, 60-64, 68-70, 74-95, 99-132, 136-144, 148-154, 162-165, 169-178, 183-188 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/__init__.py 366 299 18% 84, 87, 90, 93-96, 107, 125-132, 165-167, 172-179, 184-193, 198-205, 224-268, 312-346, 355-409, 502-563, 631, 651-661, 668-672, 708-722, 755-768, 778-783, 817-819, 845-851, 868-878, 898-903, 933-944, 1019-1045, 1059-1062, 1080-1084, 1113-1182 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_arpack.py 5 3 40% 28-30 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_encode.py 115 99 14% 30-50, 60-65, 84-102, 108-112, 115-117, 122-123, 128-144, 176-187, 215-269 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_estimator_html_repr.py 76 62 18% 39-50, 53, 61-76, 82-102, 111-143, 303-311 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_joblib.py 12 0 100% /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_mask.py 20 14 30% 9-21, 41-54 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_pprint.py 243 172 29% 79, 99, 101, 105, 185-199, 203, 207, 222-268, 276-317, 323-332, 354-379, 383-413, 418, 420, 427, 441, 446-447 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_show_versions.py 33 26 21% 24-32, 44-73, 82-93 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/_tags.py 16 13 19% 50-67 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/class_weight.py 61 55 10% 41-72, 115-181 /home/admin/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py 56 11 80% 67-68, 86-87, 101-102, 117-123 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/home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/graph_pb2.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/meta_graph_pb2.py 129 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/node_def_pb2.py 25 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/op_def_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/resource_handle_pb2.py 22 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/rewriter_config_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saved_object_graph_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/saver_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/step_stats_pb2.py 56 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/struct_pb2.py 100 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/summary_pb2.py 70 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_description_pb2.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_pb2.py 36 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/tensor_shape_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/trackable_object_graph_pb2.py 30 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/types_pb2.py 62 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/variable_pb2.py 38 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/verifier_config_pb2.py 18 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorboard/compat/proto/versions_pb2.py 14 0 100% 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298-307 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tf_utils.py 89 60 33% 32-34, 68-70, 98-115, 132-133, 150-165, 182-195, 227, 232-236, 241-249, 253-258 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/tools/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/uncompressed_module_resolver.py 37 24 35% 32-34, 37, 41-47, 65-77, 80-87 /home/admin/.local/lib/python3.8/site-packages/tensorflow_hub/version.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/_VF.py 11 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/__config__.py 7 3 57% 9, 16, 20 /home/admin/.local/lib/python3.8/site-packages/torch/__future__.py 5 1 80% 16 /home/admin/.local/lib/python3.8/site-packages/torch/__init__.py 362 162 55% 20, 27, 59-142, 148, 175-188, 207, 214-231, 262-278, 298, 307, 329-331, 377, 502, 508, 515, 533-555, 565-571, 577, 622, 634, 640, 664, 669, 674, 679, 684, 689, 694, 699, 704, 709, 714, 719, 724, 729, 734, 739, 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121, 125-127, 148-149, 187, 189, 195, 200-201, 204-211, 225-228, 232-234, 243-245, 263, 320-322, 326, 329, 332, 336, 340, 344, 349, 357-359, 365, 371, 377, 391-412, 446-449, 453-458, 462, 466, 469-477, 485-488, 491, 494, 497, 500-502, 510-513, 522-527, 530-543, 546, 587-590, 593-599, 602-603, 611-615, 618-620, 623, 626, 629, 636, 639, 651-653, 658-659, 667-669, 675-680, 690, 699, 704, 709, 714, 729-751, 754-766, 769, 772-774, 777-779, 782-783 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/conv.py 331 168 49% 51, 84, 86, 89-94, 98, 115-122, 152-166, 169-171, 287-294, 299-303, 307, 450, 580-585, 590-602, 607, 616, 632-665, 774-780, 785-795, 940, 1080-1086, 1091-1102, 1124-1127, 1146-1150, 1155-1169, 1173-1180, 1185, 1233-1251, 1254, 1302-1320, 1323, 1371-1389, 1392, 1438-1457, 1460, 1506-1525, 1528, 1574-1593, 1596 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/distance.py 25 9 64% 37-40, 43, 72-74, 77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/dropout.py 33 12 64% 13-18, 21, 58, 100, 149, 191, 233, 282 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/flatten.py 48 27 44% 40-42, 45, 48, 106-116, 119-125, 129-135, 138, 141 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/fold.py 38 15 61% 136-141, 144, 148, 287-291, 294, 298 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/instancenorm.py 72 40 44% 18-19, 23, 26, 29, 32, 38-62, 67-72, 143, 146-147, 182, 185-186, 259, 262-263, 298, 301-302, 375, 378-379, 414, 417-418 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/lazy.py 79 49 38% 15, 18, 23, 26, 29, 33, 37, 41, 45, 49, 175-178, 185-194, 208-217, 224, 231-236, 248-256, 260 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py 91 36 60% 42, 45, 100, 117, 130, 180-191, 194-197, 200, 203, 243-250, 253-254, 257-263 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/loss.py 201 83 59% 16-20, 25-27, 93, 96, 207-208, 211, 217-220, 286-289, 292, 367-369, 372, 461-462, 465, 527, 530, 610, 613, 707-711, 714, 773-774, 777, 838, 841, 918-919, 922, 978-979, 982, 1020, 1023, 1159-1161, 1164, 1210, 1213, 1262-1263, 1266, 1319-1320, 1323, 1392-1397, 1400, 1477-1481, 1484, 1591-1595, 1598, 1739-1741, 1744 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/module.py 660 395 40% 22-24, 30-38, 79-81, 111-113, 131-139, 176-184, 201, 304, 307, 310, 313, 315, 317, 319, 343, 347, 350, 352, 354, 359, 363, 382, 385, 388, 390, 392, 397, 447-465, 489-503, 527-540, 557, 572, 593, 608-611, 614-621, 667-670, 689, 708, 727, 738, 752, 763, 774, 785, 796, 808, 813, 817, 821, 911-915, 923, 943-951, 986-994, 1001-1013, 1016-1053, 1077-1079, 1100-1102, 1105-1122, 1132-1174, 1179-1192, 1217, 1222, 1227-1231, 1236, 1241-1245, 1249-1253, 1258-1266, 1275-1277, 1292-1300, 1308, 1312, 1357-1386, 1403-1407, 1435-1437, 1474, 1485-1489, 1497, 1501-1504, 1508-1515, 1519-1522, 1524, 1532, 1559, 1570, 1583-1584, 1595, 1599, 1604, 1610-1619, 1642-1643, 1665-1669, 1690-1691, 1713-1717, 1772-1773, 1807-1818, 1836, 1880-1882, 1892-1908, 1912, 1915, 1924, 1928-1949, 1952-1962, 1965-1975 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/normalization.py 97 36 63% 48-52, 55, 59, 69-73, 76, 80, 178-179, 193, 248-264, 267-269, 272, 276 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/padding.py 83 28 66% 19-20, 23, 26, 75-76, 125-126, 165-166, 174, 177, 216-217, 267-268, 319-320, 328, 331, 370-371, 421-422, 461-462, 512, 515 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pixelshuffle.py 23 8 65% 49-50, 53, 56, 99-100, 103, 106 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/pooling.py 241 104 57% 21-27, 30, 88, 162, 240, 248, 312-315, 318, 395-398, 401, 461-464, 467, 475, 534-539, 542, 613-619, 622, 699-705, 708, 712-715, 765-778, 782, 834-847, 851, 865-869, 872, 914, 969, 978-980, 983, 1016, 1058, 1101, 1108-1109, 1112, 1140, 1179, 1218 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/rnn.py 473 382 19% 21, 43-124, 127-131, 140-175, 182-191, 194-196, 199-205, 210-221, 225-226, 229-232, 235-237, 241-254, 257-292, 296, 299-304, 411-420, 425, 430, 433-499, 673, 676-683, 692-695, 703-705, 712, 719, 722-784, 898-900, 905, 910, 913-967, 983-997, 1000-1005, 1008-1010, 1072-1074, 1077-1108, 1173-1174, 1177-1197, 1264-1265, 1268-1288 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/sparse.py 130 71 45% 129-133, 142-144, 154-155, 158, 163-174, 205-218, 318-341, 344-345, 348-350, 383, 390-400, 432-447 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/transformer.py 187 144 23% 57-83, 136-149, 156, 161-163, 186-190, 203-246, 267-270, 288-299, 361-388, 391-393, 410-466, 471-475, 479-480, 523-546, 549-551, 570-580, 585-589, 594-598, 602-603, 607, 611-616 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/upsampling.py 37 17 54% 141-150, 153, 157-162, 207, 253 /home/admin/.local/lib/python3.8/site-packages/torch/nn/modules/utils.py 35 17 51% 32-38, 57-75 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/__init__.py 10 3 70% 11-14 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_functions.py 88 60 32% 14-30, 34, 41-45, 49, 56-75, 79-82, 89-104, 108, 118-124 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/_replicated_tensor_ddp_utils.py 13 7 46% 18-23, 27, 31 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/comm.py 81 70 14% 29-38, 56-58, 76-104, 126-149, 186-199, 228-241 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py 94 76 19% 17-37, 122-145, 148-169, 172, 175, 178, 181, 199-232 /home/admin/.local/lib/python3.8/site-packages/torch/nn/parallel/distributed.py 440 339 23% 46-53, 57-60, 67-79, 83-137, 159-168, 172-178, 186-193, 200-230, 237, 538-664, 667-672, 675-677, 691-766, 769-776, 780-794, 798-839, 850-860, 865-898, 905-916, 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/home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/dynamic/modules/linear.py 7 3 57% 20-22 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/conv.py 84 50 40% 29-35, 38, 48-65, 71-95, 126-130, 148, 181-185, 202, 206, 239-243, 260, 264 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/embedding_ops.py 56 38 32% 27-36, 39, 50-64, 67-72, 95-104, 107, 120-134, 137-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/qat/modules/linear.py 38 26 32% 30-34, 37, 45-69, 72-77 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/activation.py 242 222 8% 64-83, 86, 90-150, 160-219, 224-247, 305, 321-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantizable/modules/rnn.py 230 196 15% 34-48, 51-73, 76-80, 83, 93-104, 108-115, 126-128, 131-136, 140-143, 151-157, 160-209, 218-241, 277-312, 315-361, 364, 368-379, 383 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/__init__.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/conv.py 108 58 46% 19-35, 51-54, 67-71, 74, 78, 87-90, 103-107, 110, 114, 123-126, 139-143, 146, 150, 160-177, 195-198, 211-221, 224, 228, 239-242, 254-266, 269, 273, 283-286, 299-309, 312, 316 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/linear.py 23 11 52% 25-26, 29, 42-44, 48-55 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/rnn.py 262 210 20% 11, 14-22, 25-29, 32-36, 41-54, 57-79, 83, 86, 89, 92, 95, 105-107, 110, 115-146, 150-162, 172-173, 176, 179-199, 203-214, 224-225, 228, 231-251, 255-266, 274-289, 292-312, 322, 329-331, 334-341, 350-353, 365-375, 378-388, 391-453, 456, 460-471 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/sparse.py 29 12 59% 19-21, 24, 27-28, 34, 60-63, 66, 69-70, 78 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/_reference/modules/utils.py 73 61 16% 6-41, 52-55, 64-67, 76-77, 83-87, 98-112, 124-133, 136-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/__init__.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/conv.py 111 64 42% 56-67, 72, 77-84, 120-131, 136, 141-147, 184-195, 200, 205-211, 253-259, 264, 269-271, 313-319, 324, 329-331, 373-379, 384, 389-391 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/linear.py 58 43 26% 34-39, 43-55, 58, 61-66, 70-72, 83-112, 122-126 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/dynamic/modules/rnn.py 506 403 20% 12, 16-33, 37-38, 41-42, 46-47, 59-123, 126, 129-140, 147-170, 173-179, 184-191, 197-198, 202-204, 208-210, 214-216, 221-254, 258-341, 346-364, 367, 370, 391, 394, 401-426, 432-440, 446-455, 461-463, 469-474, 479-482, 486, 490-504, 616, 619, 622-625, 633-670, 677-685, 691-699, 704-706, 710-713, 717, 725-763, 766, 769-774, 777-778, 783-789, 795-841, 845-873, 877-886, 889, 892, 897-901, 907-909, 913-915, 937-938, 941, 944-962, 966, 989, 992, 995-1001, 1008, 1029, 1032, 1035-1039, 1047 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/functional.py 143 114 20% 40-42, 70-72, 89-91, 106-109, 154-168, 213-227, 273-287, 325-327, 358-363, 374-378, 390-394, 409-411, 431-441, 446-450, 460-462, 475-481, 492-494, 499-503, 516-518, 570-571, 592-593, 614-615 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/__init__.py 38 13 66% 44-50, 53, 58-60, 63, 82, 85, 89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/activation.py 94 49 48% 28-29, 32, 35, 39, 49-51, 54, 58, 62-63, 67, 78-80, 83, 87, 91-92, 96, 108-111, 114, 118, 122-123, 127, 138-140, 143, 147-148, 159-162, 165-172, 176, 180-181, 185 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/batchnorm.py 64 38 41% 8-11, 15-26, 30-43, 52-53, 56, 61-62, 67, 73, 82-83, 86, 91-92, 97, 103 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/conv.py 381 265 30% 25-30, 37, 46-80, 83, 86, 89, 92-104, 119-124, 128-129, 152-160, 166-179, 182-186, 189, 195-212, 216-238, 249-265, 316-324, 329, 332-336, 341-342, 345, 348, 353-360, 370, 416-423, 428, 431-435, 439, 442, 445, 450-456, 467, 513-521, 526, 529-533, 537, 540, 543, 548-554, 565, 577-582, 588-592, 602-623, 634-651, 699-706, 711, 714, 719-720, 723-724, 727-728, 733-735, 740, 787-794, 799, 802, 807-808, 811-812, 815-816, 821-823, 828, 876-883, 888, 891, 896-897, 900-901, 904-905, 910-912, 917 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/dropout.py 13 4 69% 15, 18, 22, 26 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/embedding_ops.py 134 95 29% 12-22, 26-29, 34-37, 40, 48-50, 54-61, 65, 93-110, 113-116, 119, 122, 125-129, 132, 135, 145-175, 179-190, 220-225, 229-234, 239, 249-276, 280-292 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/functional_modules.py 105 69 34% 34-35, 38, 43-45, 49-52, 56-58, 62-65, 69-71, 75-78, 93, 98-99, 103-104, 108-109, 113-114, 118-119, 123-125, 154-157, 160-162, 167-169, 173, 176, 181, 186-188, 192-195, 199-201, 205-208, 212-214, 218-220, 224-230 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/linear.py 141 103 27% 16-22, 26-31, 36-41, 44, 63-65, 69-86, 91, 125-147, 150, 153, 158, 161, 194-196, 203-218, 225, 228, 231, 234, 244-276, 288-296 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/normalization.py 100 59 41% 15-22, 25, 30, 34-38, 42, 58-64, 67, 72, 76-80, 93-99, 102, 107, 111-115, 119, 134-140, 143, 148, 152-156, 160, 175-181, 184, 189, 193-197, 201 /home/admin/.local/lib/python3.8/site-packages/torch/nn/quantized/modules/utils.py 50 35 30% 12, 15-31, 37-41, 48-71 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/__init__.py 10 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/clip_grad.py 34 26 24% 30-56, 68-70, 85-89 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/convert_parameters.py 29 24 17% 16-24, 36-54, 73-84 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/fusion.py 33 27 18% 7-14, 17-33, 36-43, 46-53 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/init.py 10 7 30% 44-51 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/memory_format.py 8 6 25% 64-69 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrizations.py 152 124 18% 13-17, 24-28, 45-65, 68-105, 109-168, 258-281, 292-316, 320-326, 362-370, 374-388, 393, 472-486 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/parametrize.py 223 198 11% 48-54, 58-61, 97-189, 209-254, 258-269, 281-299, 315-346, 471-552, 566-573, 601-656, 665-668, 686-728 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/rnn.py 126 90 29% 22-24, 64, 76, 82-85, 89-92, 95, 98, 101, 104, 107, 110, 113, 116, 134-142, 147, 151, 166-184, 193-195, 199-204, 244-261, 321-335, 379-396, 433-446, 481-482, 511-513 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/spectral_norm.py 138 111 20% 26-32, 35-41, 73-93, 96-102, 105, 111-112, 116-152, 160, 172-198, 206, 209-214, 273-281, 295-314 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/stateless.py 53 41 23% 12-36, 39-48, 52-55, 63-69, 81-84, 125-142 /home/admin/.local/lib/python3.8/site-packages/torch/nn/utils/weight_norm.py 52 36 31% 15-18, 22-24, 28-54, 57-61, 64, 108-109, 123-129 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/__init__.py 63 28 56% 42-47, 51-54, 348-350, 386-388, 392-394, 408-410, 414-416, 420-422, 430-432, 464-466, 473, 480, 487, 498, 509 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_constants.py 4 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_globals.py 20 6 70% 33, 37-41 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/_patch_torch.py 112 90 20% 49-72, 77-78, 84-99, 103-116, 123, 132-133, 137, 145-167, 184-222, 230-231 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_caffe2.py 151 113 25% 11-35, 39-46, 50-51, 55-56, 63-65, 70-76, 83-85, 92-105, 112-125, 130-136, 141-149, 154-160, 165, 172, 178-191, 196-212, 226-249, 253-262, 267-285, 289-301, 306-318 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_helper.py 624 503 19% 67-112, 121-123, 127-130, 134-140, 144-146, 150-157, 163, 199-232, 284-315, 324-325, 335-343, 347, 351, 355, 362, 366, 370, 379-380, 388, 395-397, 401-409, 413-418, 422-430, 435-440, 444, 453, 462, 471-479, 483-488, 492-507, 511-518, 522-537, 552-555, 559-572, 578-591, 597-604, 608-611, 625-635, 639-659, 663-684, 688-710, 714-726, 730-737, 741-752, 756-785, 790-838, 855-933, 946-952, 956-961, 965-972, 976-1000, 1004-1008, 1012-1015, 1024-1043, 1052-1062, 1066-1113, 1117-1122, 1126-1135, 1149-1165, 1169-1176, 1180-1182, 1186-1193, 1207-1229, 1248-1277, 1286-1297, 1301-1304, 1309, 1313, 1317, 1323, 1432 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset10.py 268 194 28% 24-27, 32-35, 39-55, 60, 65, 74-118, 155-179, 191-202, 218-221, 225-248, 252-287, 300, 311, 327-393, 405-430, 439, 443-447, 451-456, 460, 467-506, 526-535, 539-544, 548-553, 557-561, 576-588, 603-614 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset11.py 585 467 20% 19-38, 44-71, 76-84, 89-97, 101-121, 127, 131-237, 242-245, 264, 271-275, 280-295, 302-311, 315-316, 320-333, 337-343, 347-353, 357-358, 362, 366-378, 382, 386, 390, 394-398, 402-406, 411-414, 429-450, 462-465, 470, 477, 481, 485-487, 492-524, 529, 534-543, 553-600, 604-608, 612-614, 618-620, 632-642, 646, 650, 654-709, 714-720, 724-726, 730-777, 781-784, 788, 792-808, 812-829, 833-839, 845-866, 872-893, 905-936, 943-944, 948-954, 968-1012, 1016-1017, 1025-1045, 1050-1066, 1082-1153, 1158-1185, 1195-1206, 1215-1216, 1224-1240 /home/admin/.local/lib/python3.8/site-packages/torch/onnx/symbolic_opset9.py 2398 1890 21% 58-60, 64, 68-70, 74, 78-79, 83-91, 96-98, 102, 106, 110-113, 118-119, 124-131, 137-165, 169-189, 194, 198, 212-225, 230-232, 237-238, 243-247, 251, 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/home/admin/.local/lib/python3.8/site-packages/torch/optim/adamw.py 165 152 8% 78-91, 94-104, 114-177, 203-218, 250-313, 331-415 /home/admin/.local/lib/python3.8/site-packages/torch/optim/asgd.py 108 95 12% 32-39, 42-57, 67-113, 136-148, 174-203, 219-247 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lbfgs.py 255 240 6% 9-31, 46-180, 223-240, 243-245, 248-257, 260-266, 269, 272-273, 276-280, 290-475 /home/admin/.local/lib/python3.8/site-packages/torch/optim/lr_scheduler.py 596 506 15% 27-77, 85, 94, 99, 103, 108-115, 122-168, 196-205, 218-225, 237-245, 248-252, 279-288, 298-305, 314-322, 325-333, 365-367, 370-376, 380, 412-414, 417-423, 427-428, 462-467, 470-482, 485, 524-533, 536-546, 551, 569-570, 573-579, 583, 616-638, 641-647, 656-662, 671-678, 722-724, 727-743, 749, 779-787, 790-792, 801-807, 816-823, 879-911, 915-917, 921-943, 946-954, 959, 962-974, 977-989, 992, 995-996, 1110-1161, 1165-1171, 1174, 1177, 1180, 1190-1222, 1255-1264, 1267-1271, 1300-1343, 1464-1563, 1567-1573, 1577-1578, 1582, 1585-1616 /home/admin/.local/lib/python3.8/site-packages/torch/optim/nadam.py 123 110 11% 59-74, 77-88, 98-148, 172-190, 218-247, 264-299 /home/admin/.local/lib/python3.8/site-packages/torch/optim/optimizer.py 160 139 13% 13, 34-59, 62, 69-70, 73-81, 85-97, 100-115, 128-143, 156-210, 227-251, 264, 276-314 /home/admin/.local/lib/python3.8/site-packages/torch/optim/radam.py 114 101 11% 64-76, 79-86, 96-141, 163-178, 202-236, 251-286 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rmsprop.py 105 93 11% 69-82, 85-89, 99-154, 176-188, 214-235, 251-275 /home/admin/.local/lib/python3.8/site-packages/torch/optim/rprop.py 90 78 13% 58-64, 67-69, 79-125, 145-157, 177-198, 211-237 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sgd.py 106 94 11% 93-105, 108-112, 122-163, 185-197, 220-241, 256-301 /home/admin/.local/lib/python3.8/site-packages/torch/optim/sparse_adam.py 58 51 12% 26-51, 61-111 /home/admin/.local/lib/python3.8/site-packages/torch/optim/swa_utils.py 100 76 24% 99-110, 113, 116-132, 161-187, 233-246, 250-257, 261, 265, 269-271, 274-286 /home/admin/.local/lib/python3.8/site-packages/torch/overrides.py 212 145 32% 71-72, 303-304, 337-1330, 1355-1366, 1400-1431, 1472-1510, 1562-1634, 1645, 1662-1664, 1669-1671, 1695, 1737-1738, 1817, 1820, 1824, 1829-1831, 1839-1844, 1849, 1853, 1856, 1889, 1908, 1912, 1915 /home/admin/.local/lib/python3.8/site-packages/torch/package/__init__.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/_digraph.py 97 78 20% 14-24, 33-40, 48-53, 57-60, 64-67, 72-74, 79, 83, 87-90, 95-103, 108-116, 121-140, 144-158, 166-167 /home/admin/.local/lib/python3.8/site-packages/torch/package/_directory_reader.py 31 17 45% 11, 14, 28, 31-33, 36-39, 42-43, 48-52 /home/admin/.local/lib/python3.8/site-packages/torch/package/_importlib.py 44 36 18% 22-24, 29-33, 38-50, 60-82, 90-94 /home/admin/.local/lib/python3.8/site-packages/torch/package/_mangling.py 25 14 44% 16-22, 25-26, 34-38, 41, 53-58, 62 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_pickler.py 57 48 16% 18-28, 34-98, 102-107 /home/admin/.local/lib/python3.8/site-packages/torch/package/_package_unpickler.py 15 9 40% 15-16, 20-26 /home/admin/.local/lib/python3.8/site-packages/torch/package/_stdlib.py 19 12 37% 14-15, 19-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/find_first_use_of_broken_modules.py 10 7 30% 21-29 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/is_from_package.py 7 3 57% 13-16 /home/admin/.local/lib/python3.8/site-packages/torch/package/analyze/trace_dependencies.py 24 21 12% 17-60 /home/admin/.local/lib/python3.8/site-packages/torch/package/file_structure_representation.py 60 50 17% 13-15, 27-32, 41-43, 53-61, 64-66, 72-101, 126-132 /home/admin/.local/lib/python3.8/site-packages/torch/package/find_file_dependencies.py 70 55 21% 15-18, 21-23, 26-28, 31-32, 35-43, 46-49, 52-55, 59-99 /home/admin/.local/lib/python3.8/site-packages/torch/package/glob_group.py 31 19 39% 42-45, 48, 51, 54-55, 61-64, 70-82 /home/admin/.local/lib/python3.8/site-packages/torch/package/importer.py 101 75 26% 52, 73-133, 143-162, 169, 172, 185, 197-203, 206-224, 227-232 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_exporter.py 441 342 22% 74, 107-109, 128-152, 200-241, 256-290, 299-301, 320-350, 368-381, 393-396, 399-408, 411-415, 418-428, 434-485, 498-503, 513-559, 586-686, 696, 706-707, 724-726, 743-745, 762-764, 788, 829, 860, 874, 879-930, 933, 939-944, 947-961, 965-972, 977-979, 985-1035, 1039, 1048-1052, 1056-1058, 1061-1063, 1066-1067, 1078, 1083-1089, 1098, 1107, 1116, 1125, 1133-1136, 1146, 1161-1163 /home/admin/.local/lib/python3.8/site-packages/torch/package/package_importer.py 346 274 21% 70-111, 132-134, 147-148, 168-169, 183-264, 273, 290, 303-304, 311, 320-356, 359-371, 374-376, 382-383, 388-394, 397-402, 406-425, 429-446, 457-461, 472-502, 505-527, 535-545, 548-555, 560-574, 582-595, 598-602, 616-617, 624, 641-642, 657-658, 661-663, 668-676, 679-680, 683-704 /home/admin/.local/lib/python3.8/site-packages/torch/profiler/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/profiler/profiler.py 171 127 26% 27, 70-77, 80-81, 84, 87-98, 101-107, 110-111, 117-128, 144-145, 155-156, 163-164, 171-172, 179, 182-186, 211-232, 240, 249-266, 393-424, 464-465, 468, 471-474, 477-479, 485-497, 500-501, 504-507 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/__init__.py 14 2 86% 18-19 /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fake_quantize.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuse_modules.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/fuser_method_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/observer.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/qconfig.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quant_type.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantization_mappings.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize.py 20 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/quantize_jit.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quantization/stubs.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/quasirandom.py 66 54 18% 48-68, 85-104, 120-129, 135-137, 148-153, 156-171, 174-179 /home/admin/.local/lib/python3.8/site-packages/torch/random.py 46 33 28% 18, 23, 36-42, 49-55, 62, 85-129 /home/admin/.local/lib/python3.8/site-packages/torch/return_types.py 18 2 89% 11, 14 /home/admin/.local/lib/python3.8/site-packages/torch/serialization.py 527 356 32% 38-40, 93-114, 118-119, 123-124, 133-147, 151-157, 165-169, 178, 184, 188-190, 211, 214, 225, 230, 233, 237, 247, 250, 255-256, 259-260, 265-269, 273-277, 286-293, 299-305, 310-312, 323-325, 374-381, 385-525, 529-604, 707-711, 713, 721-726, 734-946, 957, 964-979, 987, 1013, 1039-1040 /home/admin/.local/lib/python3.8/site-packages/torch/sparse/__init__.py 24 9 62% 11-12, 209-218 /home/admin/.local/lib/python3.8/site-packages/torch/special/__init__.py 37 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/storage.py 475 260 45% 13-14, 31, 70-71, 76, 79, 82, 87, 93-95, 98, 106, 110-113, 116-121, 125, 129, 133, 137, 141, 145, 149, 153, 157, 161, 165, 169, 173-177, 188-195, 200-207, 210, 218, 259-278, 281-284, 292-293, 297, 303-357, 373, 379, 384, 389, 396, 403-424, 431, 442, 445, 448-470, 473-490, 497-516, 519-520, 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84-164 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_deprecated.py 59 30 49% 32-36, 55-57, 72-80, 116-141 /home/admin/.local/lib/python3.8/site-packages/torch/testing/_legacy.py 80 38 52% 45-47, 57, 61, 65, 68, 72, 75, 79, 83, 86, 90, 93, 97, 100, 104, 107, 111, 123-130, 133, 137, 141, 145-150, 154, 158 /home/admin/.local/lib/python3.8/site-packages/torch/torch_version.py 36 20 44% 21-27, 30, 33, 58-71, 74-81 /home/admin/.local/lib/python3.8/site-packages/torch/types.py 44 11 75% 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75 /home/admin/.local/lib/python3.8/site-packages/torch/utils/__init__.py 11 2 82% 10, 15 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_crash_handler.py 17 8 53% 9, 12-17, 21, 25 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_mode_utils.py 60 38 37% 27-33, 48, 52, 59, 66-102, 110-132 /home/admin/.local/lib/python3.8/site-packages/torch/utils/_pytree.py 118 79 33% 43, 46, 49, 52, 55, 58, 61, 64, 74-81, 84-86, 90, 100-103, 106, 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33-47, 50 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/callable.py 21 7 67% 15, 49-52, 55, 58 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combinatorics.py 24 11 54% 44-49, 52-54, 59-60, 63 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/combining.py 54 35 35% 35-42, 45-51, 54-56, 80-87, 90-96, 99-101 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/grouping.py 39 25 36% 40-46, 49-59, 62-70 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/map/utils.py 17 9 47% 32-42, 45, 48 /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/__init__.py 0 0 100% /home/admin/.local/lib/python3.8/site-packages/torch/utils/data/datapipes/utils/common.py 116 91 22% 21-22, 30-39, 50-76, 80-90, 94-101, 138-173, 183, 186-187, 190-192, 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75-109, 114-133, 139-168, 179-196, 199, 210-219, 222, 225-241 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kinetics.py 100 72 28% 20, 116-156, 160-167, 176-197, 211-229, 236, 239, 242-248, 312-323 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/kitti.py 58 39 33% 61-84, 105-109, 112-128, 131, 135, 139-142, 147-154 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lfw.py 125 96 23% 42-58, 61-63, 66-72, 75-82, 85, 88, 91, 123-126, 129-144, 147-151, 161-170, 173, 205-207, 210-235, 245-255 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/lsun.py 94 75 20% 18-31, 34-50, 53, 79-93, 96-136, 146-161, 164, 167 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/mnist.py 243 152 37% 65-66, 70-71, 75-76, 80-81, 91-104, 107-111, 118-119, 122-128, 138-150, 153, 157, 161, 165, 168, 176-195, 198-199, 294-298, 302, 306, 310, 314, 318, 321, 324, 329-339, 421-428, 432-433, 437-438, 441, 444-461, 467-474, 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61-96, 99-100, 103-104, 110-116, 119, 122-123 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sbu.py 59 45 24% 36-58, 68-77, 81, 85-89, 93-114 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/semeion.py 43 28 35% 37-51, 61-73, 76, 79-83, 86-91 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stanford_cars.py 49 38 22% 41-74, 77, 81-88, 91-111, 118-121 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/stl10.py 104 79 24% 55-88, 91-100, 111-126, 129, 132-145, 148-154, 157-161, 164, 168-176 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/sun397.py 36 23 36% 36-51, 56, 59-68, 71, 74-76 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/svhn.py 44 30 32% 61-91, 101-113, 116, 119-122, 125-126, 129 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/ucf101.py 45 32 29% 71-101, 105, 108-118, 121, 124-130 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/usps.py 34 23 32% 52-70, 80-92, 95 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/utils.py 235 185 21% 38-45, 49-50, 54-63, 70-74, 78, 82-86, 90-100, 106-115, 130-167, 178-182, 195-199, 203-215, 229-280, 288-289, 299-302, 333-361, 377-393, 411-429, 440-450, 454, 466-485 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/video_utils.py 199 164 18% 28-29, 41-49, 62, 65, 68, 75, 122-140, 143-166, 169-173, 177-182, 185-193, 212-232, 245-255, 258, 261, 267, 274-279, 283-291, 306-379, 382-407, 411-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/vision.py 60 42 30% 38-54, 64, 67, 70-78, 81-82, 85, 90-91, 94-98, 101-102, 105-111 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/voc.py 94 61 35% 9-10, 79-127, 130, 158, 168-174, 203, 213-219, 223-237 /home/admin/.local/lib/python3.8/site-packages/torchvision/datasets/widerface.py 95 76 20% 65-81, 94-103, 106, 109-110, 113-158, 161-169, 173-180, 183-194 /home/admin/.local/lib/python3.8/site-packages/torchvision/extension.py 48 12 75% 15, 27-28, 33, 49, 56-57, 66, 84-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/__init__.py 12 2 83% 9-10 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_load_gpu_decoder.py 6 1 83% 6 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/_video_opt.py 154 124 19% 13, 31-32, 58-65, 70-71, 87-105, 111-120, 188-217, 226-252, 259-262, 335-369, 380-408, 418-423, 432-484, 490-505 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/image.py 72 48 33% 12-13, 45-48, 60-62, 82-85, 103-106, 121-124, 157-164, 182-188, 202-205, 227-230, 249-252, 256-257 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video.py 207 181 13% 21-31, 41-42, 46, 81-139, 151-219, 225-234, 263-347, 351-356, 360-364, 384-415 /home/admin/.local/lib/python3.8/site-packages/torchvision/io/video_reader.py 48 33 31% 9-10, 17-18, 24, 94-111, 125-133, 136, 151-152, 160, 179-181 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/__init__.py 21 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_api.py 66 38 42% 53-60, 63, 66, 86-107, 121-142 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_meta.py 6 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/_utils.py 104 67 36% 51-64, 67-73, 83-89, 125-126, 132-142, 174-228, 236-240, 244-247, 252-256 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/alexnet.py 37 17 54% 19-37, 48-52, 106-116 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/convnext.py 131 84 36% 32-35, 46-60, 63-66, 77-79, 82-87, 101-167, 170-173, 176, 186-194, 299-308, 331-340, 361-370, 393-402 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/densenet.py 158 98 38% 36-46, 49-51, 55-58, 62-65, 69, 73, 78-94, 109-118, 121-125, 130-134, 164-211, 214-219, 227-238, 249-257, 360-362, 385-387, 410-412, 435-437 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/__init__.py 7 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/_utils.py 231 193 16% 22-23, 41-71, 87-119, 136-137, 140-144, 155-160, 163-181, 193-224, 238, 253-271, 286-301, 341-346, 359-386, 397-415, 420, 423-431, 447-449, 464-479, 484, 506-510, 521-538 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/anchor_utils.py 125 105 16% 40-50, 65-74, 77, 80, 85-113, 116-133, 163-182, 187-202, 206, 212-236, 239-247, 250-268 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/backbone_utils.py 82 64 22% 42-54, 57-59, 112-113, 125-146, 158-174, 193-194, 205-242 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/faster_rcnn.py 188 134 29% 38-40, 201-280, 293-296, 299-304, 322-341, 355-357, 360-369, 547-569, 613-644, 656-685, 739-751, 812-820 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585-673, 794-819, 862-891 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/roi_heads.py 481 441 8% 28-49, 69-79, 91-94, 109-126, 131-162, 168-213, 220-231, 244-295, 300-326, 331-342, 347-360, 368-383, 389, 394-400, 405-425, 429-461, 466-471, 476-489, 522-548, 551-557, 560-566, 570-601, 605-610, 614-616, 620-628, 636-666, 676-725, 742-876 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/rpn.py 175 149 15% 29-41, 53-62, 73-79, 83-86, 90-111, 161-181, 184-186, 189-191, 197-229, 232-240, 250-297, 314-337, 363-391 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssd.py 273 223 18% 49-53, 58-60, 63, 71-73, 80-87, 90-103, 108-112, 117-121, 201-242, 248-251, 260-319, 327-410, 415-461, 466-535, 539-548, 552-568, 643-679 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/ssdlite.py 119 79 34% 33, 49-51, 74-78, 85-87, 90, 100-104, 109-113, 125-148, 152-161, 169-184, 262-328 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/detection/transform.py 185 159 14% 14-16, 22, 32-71, 96-105, 110-146, 149-157, 165-166, 173-194, 200-218, 221-225, 228-245, 253-267, 270-275, 279-293, 297-309 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/efficientnet.py 265 159 40% 59, 76-81, 85, 100-102, 113-162, 165-169, 179-223, 226-230, 255-342, 345-352, 355, 366-374, 382-429, 754-757, 782-785, 810-813, 838-841, 866-869, 894-897, 930-933, 966-969, 1003-1006, 1040-1043, 1077-1080 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/googlenet.py 185 143 23% 42-100, 103-108, 112-163, 167-170, 173-181, 196-212, 218-224, 227-228, 239-246, 250-263, 268-270, 273-275, 319-342 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/inception.py 299 243 19% 38-93, 96-101, 105-155, 159-162, 165-173, 180-192, 195-208, 211-212, 217-224, 227-236, 239-240, 247-263, 266-282, 285-286, 291-300, 303-313, 316-317, 322-336, 339-360, 363-364, 371-378, 382-395, 400-402, 405-407, 457-475 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mnasnet.py 119 73 39% 38-45, 60-63, 70-77, 84-87, 93-94, 113-156, 159-162, 174-209, 306-314, 339-341, 366-368, 393-395, 420-422 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenet.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv2.py 105 72 31% 23-32, 43-78, 81-84, 112-181, 186-191, 194, 265-275 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/mobilenetv3.py 138 97 30% 29-33, 54-61, 65, 76-124, 127-130, 155-223, 226-233, 236, 242-285, 295-303, 398-401, 428-431 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/__init__.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/_utils.py 25 18 28% 10-17, 21-23, 33-45 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/optical_flow/raft.py 288 219 24% 31-59, 62-68, 75-92, 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/home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/mobilenetv3.py 88 53 40% 34-36, 39, 42, 54-72, 86-87, 90-93, 104-106, 109-112, 115-122, 133-158, 231-234 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/resnet.py 120 70 42% 39-40, 43-57, 60-62, 67-70, 73-88, 91-95, 100-103, 106-112, 121-124, 135-149, 315-317, 364-366, 413-417, 456-460 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/shufflenetv2.py 86 43 50% 37-38, 41-49, 55-57, 60-63, 75-82, 99-113, 253-254, 306-307, 351-352, 396-397 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/quantization/utils.py 30 24 20% 8-18, 22-42, 48-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/regnet.py 273 142 48% 62, 81-105, 122-141, 144-148, 168-182, 195-200, 234-266, 276, 287-293, 307-374, 377-384, 393-402, 1123-1126, 1148-1151, 1173-1178, 1200-1205, 1227-1232, 1254-1259, 1281-1286, 1308-1313, 1335-1338, 1360-1363, 1389-1392, 1418-1421, 1447-1450, 1476-1479, 1505-1508 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/resnet.py 236 150 36% 42, 56, 73-87, 90-105, 128-141, 144-163, 178-223, 233-264, 268-282, 285, 295-303, 668-670, 693-695, 724-726, 755-757, 786-788, 813-817, 842-846, 870-874, 904-907, 937-940 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/__init__.py 3 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/_utils.py 27 18 33% 14-18, 21-37 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/deeplabv3.py 127 81 36% 50, 61-66, 71, 79-82, 87-101, 109-113, 121-128, 203-218, 257-273, 312-328, 365-381 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/fcn.py 64 36 44% 38-47, 103-110, 152-168, 210-226 /home/admin/.local/lib/python3.8/site-packages/torchvision/models/segmentation/lraspp.py 70 44 37% 38-41, 44-51, 56-68, 71-79, 83-93, 157-175 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/home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ciou_loss.py 25 20 20% 45-71 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/deform_conv.py 65 50 23% 63-92, 126-151, 154-159, 170, 182-195 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/diou_loss.py 32 26 19% 45-57, 66-87 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/drop_block.py 68 52 24% 28-52, 74-102, 114-119, 129, 132-133, 145, 155 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/feature_pyramid_network.py 100 80 20% 33, 84-110, 122-134, 149-156, 163-170, 184-204, 218-220, 229-235, 243-249 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/focal_loss.py 18 14 22% 35-51 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/giou_loss.py 24 20 17% 43-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/misc.py 93 67 28% 27-33, 45-49, 56-62, 65, 85-114, 153, 202, 237-243, 246-250, 253-254, 282-298, 309-310, 313 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/poolers.py 123 92 25% 20-34, 45, 68-72, 80-85, 89-96, 101-107, 114-135, 140-144, 169-228, 278-288, 291-292, 295-296, 303-304, 325-331, 341 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_align.py 29 18 38% 46-57, 71-75, 78, 81-88 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/ps_roi_pool.py 28 17 39% 40-49, 58-61, 64, 67-68 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_align.py 31 18 42% 53-61, 78-83, 86, 89-97 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/roi_pool.py 30 17 43% 42-51, 60-63, 66, 69-70 /home/admin/.local/lib/python3.8/site-packages/torchvision/ops/stochastic_depth.py 34 24 29% 26-44, 56-59, 62, 65-66 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/__init__.py 2 0 100% /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/_pil_constants.py 18 8 56% 17-25 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1436-1441, 1458-1463, 1481-1486 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_pil.py 261 192 26% 19, 26-33, 38-40, 45-50, 55-58, 63-66, 71-76, 81-86, 91-96, 101-120, 130-142, 153-222, 234-237, 249, 251, 254, 256-275, 278, 293-311, 322-327, 340-344, 355-360, 365-378, 383-385, 390-392, 397-399, 404-409, 414-416, 421-423 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py 555 501 10% 10, 14-15, 19-21, 25-28, 33-34, 38-44, 48-59, 63-65, 69-117, 121-123, 127-129, 133-142, 146-162, 166-173, 177-190, 194-219, 223-233, 237-253, 257-259, 263-298, 302-319, 326-350, 354-370, 374-426, 436-503, 515-542, 546-558, 562-571, 576-603, 619-629, 635-642, 653-675, 685-693, 704-722, 728-745, 749-755, 761-764, 768-790, 795-803, 808-817, 822-832, 836-855, 859-869, 874-891, 899-912, 916, 921-933, 937-960, 964-970 /home/admin/.local/lib/python3.8/site-packages/torchvision/transforms/transforms.py 745 571 23% 88-90, 93-95, 98-103, 124, 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/home/admin/.local/lib/python3.8/site-packages/torchvision/utils.py 276 245 11% 57-132, 154-160, 201-266, 295-346, 378-418, 436-453, 468-488, 501-535, 539-540, 562 /home/admin/.local/lib/python3.8/site-packages/torchvision/version.py 5 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_dist_ver.py 1 0 100% /home/admin/.local/lib/python3.8/site-packages/tqdm/_monitor.py 45 12 73% 42-45, 63, 72-86, 95 /home/admin/.local/lib/python3.8/site-packages/tqdm/_tqdm_pandas.py 10 6 40% 12-24 /home/admin/.local/lib/python3.8/site-packages/tqdm/asyncio.py 53 36 32% 24-34, 37, 40-52, 55, 62-67, 75-81, 88 /home/admin/.local/lib/python3.8/site-packages/tqdm/auto.py 14 3 79% 27-28, 37 /home/admin/.local/lib/python3.8/site-packages/tqdm/autonotebook.py 12 5 58% 16-20 /home/admin/.local/lib/python3.8/site-packages/tqdm/cli.py 188 173 8% 17-40, 54-97, 151-311 /home/admin/.local/lib/python3.8/site-packages/tqdm/gui.py 10 1 90% 181 /home/admin/.local/lib/python3.8/site-packages/tqdm/std.py 696 372 47% 46-49, 127-128, 154-161, 165, 169-184, 187-211, 240-241, 390-398, 418, 437-439, 538, 542-547, 565-566, 573-574, 581-582, 587-588, 615-646, 650-660, 702-705, 715-717, 722-726, 735-756, 761, 805-950, 969, 975, 984, 987-995, 998-1002, 1031, 1034, 1037, 1044, 1047, 1080-1083, 1107-1111, 1114, 1122-1130, 1133-1134, 1137, 1140-1146, 1170-1172, 1192-1194, 1225-1264, 1280, 1284, 1292-1294, 1307-1308, 1312-1324, 1340, 1344-1345, 1355-1359, 1371-1381, 1393-1395, 1399-1401, 1416-1432, 1438-1440, 1444-1445, 1451, 1454, 1483-1486, 1489, 1495, 1498, 1515-1520, 1525 /home/admin/.local/lib/python3.8/site-packages/tqdm/utils.py 175 92 47% 22, 28-31, 70, 81-96, 108-109, 112-113, 119, 122, 128, 131, 134, 142, 146-149, 169-170, 176, 179, 196-209, 231-248, 254-260, 268-269, 273-278, 389-398 /home/admin/.local/lib/python3.8/site-packages/tqdm/version.py 8 6 25% 4-9 /home/admin/.local/lib/python3.8/site-packages/typing_extensions.py 1914 1300 32% 158, 165-166, 170-171, 189, 194-196, 199-201, 213-215, 223, 229, 256-263, 267, 272, 279, 290-294, 297, 306, 313-315, 319-325, 348-350, 387-395, 400-406, 410, 432, 451, 466-490, 542-545, 551, 558, 561-562, 576-578, 600, 612-635, 640-667, 674, 685-706, 731, 753, 773-774, 780, 791-797, 806, 815, 824, 833, 841, 852, 863, 869-874, 889, 895, 906, 909, 915, 926, 929, 955-957, 964-981, 994-1096, 1102, 1165-1210, 1215, 1229-1231, 1235, 1251, 1255, 1260-1280, 1314-1333, 1340-1345, 1352-1403, 1407-1410, 1422-1426, 1429-1431, 1434, 1438, 1443-1447, 1450, 1488, 1492-1503, 1506, 1514-1515, 1545, 1550, 1564-1573, 1578, 1581-1593, 1612-1613, 1618-1620, 1633, 1637, 1648-1672, 1675, 1680-1681, 1689, 1692, 1707, 1710, 1713-1715, 1730, 1733, 1736-1738, 1742, 1748-1791, 1849, 1853, 1857-1871, 1874-1882, 1885, 1888, 1891, 1895, 1906-1916, 1927-1929, 1932-1933, 1937, 1941, 1945, 1952-1959, 1963-2063, 2072-2091, 2097-2107, 2112, 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4200-4286, 4290, 4296-4379, 4387-4430, 4464-4534 /home/admin/.local/lib/python3.8/site-packages/uritemplate/__init__.py 8 0 100% /home/admin/.local/lib/python3.8/site-packages/uritemplate/api.py 11 3 73% 43, 66, 85 /home/admin/.local/lib/python3.8/site-packages/uritemplate/orderedset.py 59 40 32% 28-32, 35, 38, 42-47, 52-55, 59-63, 67-71, 74-78, 81-83, 86, 89-92 /home/admin/.local/lib/python3.8/site-packages/uritemplate/template.py 48 33 31% 30-34, 72-83, 86, 89, 92-94, 97, 102-120, 147, 169 /home/admin/.local/lib/python3.8/site-packages/uritemplate/variable.py 195 171 12% 56-71, 74, 77, 89-122, 130-143, 153-190, 204-240, 250-295, 304-325, 356-386, 392-399, 403, 407, 411-413, 417-419 /home/admin/.local/lib/python3.8/site-packages/zipp.py 123 73 41% 28, 47-50, 62, 73-75, 78-79, 82, 89-92, 100-111, 121-124, 127-130, 223-224, 232-242, 246, 250, 254, 258, 262, 265-266, 269-270, 273, 276, 279, 282, 285, 288-291, 294, 297, 300-301, 307-312 /home/admin/mtr/.credentials/credentials.py 1 0 100% /home/admin/workarea/git/Velours/python/dev/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/dev/angular_coefficients_to_crops.py 371 201 46% 12-41, 49-51, 66-68, 79-91, 97-104, 109-179, 185-186, 188-189, 194, 232, 237-238, 271, 290-304, 312-313, 316-317, 331, 346, 351-354, 365-367, 406, 417-504 /home/admin/workarea/git/Velours/python/dev/conditional_crop_copy.py 414 151 64% 14, 24, 29, 35-39, 51, 55-56, 83, 93-94, 103-104, 106, 113-114, 118, 121, 126-129, 136, 150, 157, 163, 167, 172-173, 177, 179, 182-231, 240-241, 270-274, 276-279, 289-295, 298-299, 304-306, 311-312, 321-323, 340, 343-344, 358-370, 400, 408, 418-419, 422-423, 435, 437, 442-445, 456-458, 471-473, 506, 509, 512, 524-540 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py 477 245 49% 27-28, 31-36, 72-77, 83-84, 87-88, 94, 99-106, 135-147, 156-157, 162-175, 223-227, 245-248, 251, 254-261, 268, 279-280, 296-297, 302-312, 316-333, 337-357, 362-370, 404-405, 407-408, 411-412, 414, 420, 423-431, 437-438, 440, 459-466, 498-508, 543-578, 585-586, 593, 611, 655-656, 664-671, 678-771 /home/admin/workarea/git/Velours/python/dev/poly_crop_reduction.py 238 157 34% 9-20, 40, 45, 54-56, 58-59, 117, 119-120, 127-168, 172-226, 229-244, 260-310, 330-381 /home/admin/workarea/git/Velours/python/file_uploader.py 73 35 52% 14-15, 23-24, 28-30, 36-37, 54-56, 62-64, 70-80, 84-95, 98 /home/admin/workarea/git/Velours/python/misc/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/misc/split_time_score.py 929 713 23% 30-47, 51-60, 67-224, 311, 314-329, 353-354, 368-376, 386-387, 412-413, 420-424, 432, 441-442, 447-448, 457-463, 468-499, 515, 528, 533-535, 560-594, 600-644, 774-795, 800, 811-813, 836-838, 840, 843, 861, 863, 883-944, 958-1806 /home/admin/workarea/git/Velours/python/mtr/Gan2/pre_ops.py 265 175 34% 14-16, 19-21, 24, 27, 30, 33-35, 51-52, 76-86, 89-105, 141-201, 215-293, 317-320, 322-325, 327-334, 337, 361-415 /home/admin/workarea/git/Velours/python/mtr/Rubbia_Report.py 577 544 6% 95-134, 141-202, 211-225, 234-252, 260-300, 309-315, 323-338, 352-379, 387-394, 397-429, 437-446, 450-465, 471-476, 481-527, 530-541, 545-607, 612-673, 677-771, 774-791, 797-804, 808-828, 832-930 /home/admin/workarea/git/Velours/python/mtr/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/cnn/classifier_new.py 289 77 73% 24-39, 124, 184, 213-215, 221, 225-227, 240, 245-254, 263-270, 277, 291, 337-338, 354, 356-357, 365-369, 378-379, 395, 427-430, 456-457, 465, 485, 507, 522-523, 536-550 /home/admin/workarea/git/Velours/python/mtr/cnn/ordonner.py 73 39 47% 20-29, 36, 44, 54, 66-101, 104 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelConfig.py 63 45 29% 15-18, 23-26, 30, 35-48, 54-68, 73-77, 81-85, 88-95, 98, 101 /home/admin/workarea/git/Velours/python/mtr/database_queries/CacheModelData_queries.py 180 77 57% 19, 26, 35-42, 61, 66-82, 102, 111-133, 144, 148, 150, 154-159, 161-162, 164, 166, 168, 171-172, 205-206, 226-227, 231-232, 240-256, 293 /home/admin/workarea/git/Velours/python/mtr/database_queries/CachePhotoData_queries.py 364 90 75% 35-37, 56, 58, 63, 88, 101-110, 117, 119, 133, 139-141, 149-150, 160, 169, 182-184, 201-202, 225-227, 253-255, 275-277, 288-289, 304, 327-333, 337, 350-352, 378-387, 397-398, 489-490, 501, 517, 524-529, 537, 594-597, 618-620, 634-654 /home/admin/workarea/git/Velours/python/mtr/database_queries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/admin_queries.py 457 380 17% 32-39, 44-50, 56-66, 71-87, 92, 96-99, 102-116, 120-135, 138-143, 146-148, 156-165, 168-177, 180-187, 192-206, 211-227, 232-250, 254-271, 275-291, 294-295, 298-299, 302-308, 326-331, 334-337, 340-349, 353-357, 360-367, 370-376, 379-389, 392-399, 402-404, 407-414, 417-430, 433-444, 447-469, 476, 485, 488-495, 498-504, 507-510, 514-518, 522-540, 543-548, 551-556, 559-564, 568-577, 580-588, 591-600, 603-612, 615-621, 625-648 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_admin_tools.py 87 53 39% 27-28, 30-34, 45, 61, 64, 76-92, 97-105, 110-137, 142, 147-163, 166-171 /home/admin/workarea/git/Velours/python/mtr/database_queries/classification_queries.py 291 200 31% 22-42, 45-49, 52-71, 74-82, 85-91, 94-98, 101-106, 109-117, 124-134, 139-148, 152-159, 162-172, 176-197, 200-220, 223-233, 236-248, 253-261, 267-283, 301-363, 379, 382, 390, 411, 414, 423, 489-511, 514-528 /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/database_objet/objet_thcl.py 146 114 22% 32-50, 56-65, 70-77, 81, 84, 87, 90, 93, 96, 99, 102, 105, 108, 111, 114, 117, 120, 123, 126, 129-132, 138-139, 143-147, 152-171, 177-196, 200-202, 205-212, 226-232, 237-269 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_queries.py 1475 754 49% 44, 57, 69, 87, 95-97, 102, 121-127, 131-133, 149-153, 162-170, 186-295, 305-308, 311-316, 319-326, 329-348, 351-359, 364, 369, 391, 394-397, 406-409, 420-461, 481, 484-487, 503-523, 528, 534-541, 551-572, 577, 584, 587, 590, 597, 630, 646, 663, 744, 751, 764, 801, 831, 868, 873-874, 906, 912, 960, 964, 968-969, 977, 979, 989-990, 994, 1006, 1011, 1016-1017, 1024-1044, 1083, 1090, 1135, 1144, 1158, 1164, 1179, 1191-1195, 1206, 1217, 1228-1232, 1250-1310, 1342-1349, 1366-1374, 1384-1396, 1400-1407, 1410-1416, 1419-1427, 1432-1439, 1449, 1456, 1460-1473, 1483, 1486-1493, 1505-1506, 1516-1517, 1524-1534, 1541, 1548-1554, 1556, 1568-1573, 1585-1590, 1602-1607, 1613-1617, 1624-1630, 1635-1649, 1655-1663, 1671, 1678-1680, 1685, 1691-1710, 1714-1729, 1733-1753, 1757-1775, 1781-1803, 1808-1818, 1821-1829, 1835-1852, 1858-1868, 1877, 1881, 1891-1908, 1912-1920, 1923-1927, 1945, 1950, 1966, 1979, 1990, 2022, 2047, 2075-2083, 2087-2095, 2100-2109, 2112-2121, 2124-2129, 2137, 2157, 2175, 2200, 2204-2211, 2257-2281, 2293-2295, 2299-2308, 2313-2331, 2334-2342, 2364, 2366, 2369-2371, 2385-2386, 2394, 2413-2444, 2449-2477, 2481-2487, 2491-2510, 2514-2523, 2527-2531, 2540, 2547, 2575, 2578, 2582-2609, 2644, 2650, 2654-2690, 2706-2708, 2713-2728, 2739-2775, 2784-2820 /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/database_queries/datou_utils/util_portfolio_hashtag_ids.py 213 95 55% 18-19, 23-24, 29-128, 143, 146-147, 150, 154, 160-161, 169, 217-218, 223-226, 238-239, 276-277, 301, 304-305, 310, 313-314, 328, 331-332, 336, 339-340 /home/admin/workarea/git/Velours/python/mtr/database_queries/descriptor_queries.py 354 238 33% 23-42, 56, 63, 67, 73, 77, 82-103, 106-145, 163, 166, 169, 184, 218, 226-264, 270-301, 304-321, 333, 338, 349-352, 360-387, 390-400, 404-407, 412-435, 444-471, 474-477, 480-495, 499-556 /home/admin/workarea/git/Velours/python/mtr/database_queries/general_queries.py 148 61 59% 12-13, 33-34, 36-37, 45-46, 49, 59-61, 74, 83-95, 103-114, 122-133, 137-140, 151, 163-167, 182 /home/admin/workarea/git/Velours/python/mtr/database_queries/graph_nodes_queries.py 77 60 22% 28-34, 38-54, 59-130 /home/admin/workarea/git/Velours/python/mtr/database_queries/hashtag_queries.py 158 103 35% 46-50, 64-65, 72, 80-91, 94-110, 113-125, 128-133, 136-142, 145-155, 158-165, 168-183, 188, 196-207, 211-218, 221-226, 229-235 /home/admin/workarea/git/Velours/python/mtr/database_queries/mission_queries.py 520 478 8% 26-38, 42-250, 255-272, 275-314, 317-414, 418-430, 433-445, 448-460, 463-475, 479-491, 495-507, 510-522, 525-548, 551-552, 555-567, 570-582, 586-622, 625-644, 647-662, 665-671, 674-681, 697-741, 747-756, 773-799, 803-810, 815-822, 828-838, 841-843, 848-855, 859-873 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_insert_queries.py 105 81 23% 30-71, 79, 84-91, 94-103, 106-113, 118-138, 141-145, 149-163, 173-192, 203-218 /home/admin/workarea/git/Velours/python/mtr/database_queries/photo_retrieval_queries.py 558 401 28% 12, 51-61, 71-75, 96-101, 107-123, 129-142, 148-161, 180-181, 188, 199-200, 212, 217, 221, 224-226, 229-231, 234-237, 248, 254, 261-266, 269, 271, 274, 277-278, 288-326, 332-348, 351-425, 428-475, 481-492, 495-544, 547-548, 555-605, 608-631, 634-668, 675, 683, 695, 703, 711, 714, 719, 724-742, 750, 756-758, 761, 770, 774-776, 781-787, 790, 796-800, 805-826, 832-849, 852-864, 868-922, 946, 968, 975-986, 1002 /home/admin/workarea/git/Velours/python/mtr/database_queries/portfolio_queries.py 511 261 49% 39, 41, 56-72, 88, 97-114, 122, 130, 134, 140-158, 164, 170, 172-174, 176, 179, 188, 192, 198, 202-212, 215-222, 225-235, 240-255, 261, 270, 274-284, 287-299, 302-311, 321, 325, 339, 351, 354, 360-361, 365, 369-375, 378-383, 389, 393-397, 400-410, 425, 441, 447, 473-497, 516-517, 525, 548-571, 576-584, 589, 594, 598-608, 616, 620-622, 630, 637, 642-662, 684, 717-748, 750-759, 780-781, 783-786, 793-794, 796-799 /home/admin/workarea/git/Velours/python/mtr/datou/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/calcul_brightness_image.py 76 46 39% 4, 7-8, 22-24, 42-68, 71-78, 81-88, 91-98 /home/admin/workarea/git/Velours/python/mtr/datou/count_refus.py 64 7 89% 15, 61-69, 72-73, 94 /home/admin/workarea/git/Velours/python/mtr/datou/darker_image.py 39 4 90% 15, 19, 24, 60 /home/admin/workarea/git/Velours/python/mtr/datou/data_augmentation_imgaug.py 244 194 20% 16, 19-22, 25, 27-176, 188-193, 203, 241-303 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py 1753 1005 43% 43-44, 75-101, 108-109, 112-113, 117-118, 153, 158, 188-189, 206-209, 212-213, 227-229, 241-243, 246-247, 277, 302-306, 311, 328-332, 335, 375, 397-398, 434, 462-463, 481-495, 509-516, 522-573, 580-624, 652-653, 660-663, 674-677, 700-702, 721, 729-730, 738-739, 773-776, 810, 818, 821, 823-825, 833-853, 863-870, 886-942, 968-1161, 1165, 1171-1174, 1178-1180, 1185-1186, 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2621, 2635, 2638, 2640, 2642, 2644, 2648, 2650, 2656, 2658, 2660, 2663-2667, 2701-2703, 2718-2720, 2736, 2742-2744, 2746, 2751, 2760-2762, 2771-2774, 2783-2787, 2803, 2808, 2811, 2819-2833, 2837-2843, 2855-2873 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_object.py 478 150 69% 16-23, 35-37, 46, 51-62, 101-122, 178, 194, 212, 215, 222-246, 252-290, 317, 335, 363-369, 374, 376, 385, 389, 396, 400, 412, 495, 499-500, 512-513, 522-523, 570, 579, 589, 616, 635-652, 660, 675-679, 688-689, 694, 723-743, 747-771 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_data_increase.py 204 104 49% 32, 34-35, 93, 102, 125-162, 214-216, 221-294, 297-339 /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib_step_save.py 1287 808 37% 24, 33-38, 45, 49-96, 101-145, 150, 155, 160-178, 183, 200-260, 269-305, 316, 324-325, 339-344, 347, 349, 361, 368-369, 374-436, 445-486, 489-553, 569, 580, 582, 593, 595, 604-608, 613-619, 655, 669-670, 674, 676, 695, 712-717, 722, 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/home/admin/workarea/git/Velours/python/mtr/datou/detect_blur_image.py 109 72 34% 12-15, 18-20, 24-34, 55-64, 77, 87-145 /home/admin/workarea/git/Velours/python/mtr/datou/image_blanchir.py 30 2 93% 27, 31 /home/admin/workarea/git/Velours/python/mtr/datou/image_temperature.py 22 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_deprecated.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_end_or_aggreg.py 484 469 3% 17, 19, 21, 24, 27, 34-274, 288-767, 908-918 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_initialisation.py 372 326 12% 15-244, 249-267, 284-289, 304, 313-314, 317-331, 337-346, 358-360, 376-558 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_post_processing.py 1061 486 54% 52, 61-65, 72-77, 83-95, 99, 115-119, 141-142, 149-153, 161, 163, 179, 197-198, 228, 234-235, 256, 259-264, 273-279, 327-328, 356-363, 369, 371-449, 454-458, 481-482, 497-503, 506, 511-513, 538, 541-542, 547, 555, 568, 600-601, 604-605, 610-612, 615, 638-647, 653-655, 665, 675-681, 684-685, 689-694, 700-701, 704-705, 714-721, 724-732, 735-736, 752-757, 763-797, 802-815, 821-823, 843-845, 850-857, 874-924, 932-1006, 1011-1081, 1122-1124, 1128-1129, 1189, 1210-1231, 1243, 1249-1255, 2536-2538, 2541-2542, 2545-2552, 2562, 2569-2581, 2584, 2588-2592, 2599-2606, 2667-2669, 2762-2763, 2777-2778, 2789-2791, 2798-2808, 2816, 2848, 2859 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py 1403 793 43% 34-89, 124, 127, 133, 136-137, 150, 157-161, 172-177, 188-193, 233-235, 260-275, 281, 283, 289, 350, 354, 362-376, 391-399, 408-422, 425-426, 428-430, 432, 438, 440, 447, 485, 490-491, 497, 506, 510-696, 703-838, 843-885, 898-899, 908, 913-920, 928-929, 932-934, 956-960, 1003-1010, 1014-1021, 1024-1026, 1034, 1076-1077, 1087, 1095-1105, 1109-1111, 1119-1133, 1142-1143, 1156, 1174-1176, 1180, 1199-1200, 1204, 1220-1231, 1242, 1245-1246, 1251, 1255, 1259-1306, 1322-1323, 1344-1345, 1362-1459, 1485, 1502, 1508-1551, 1580-1581, 1586, 1596, 1620, 1623-1624, 1663-1666, 1670, 1677-1678, 1683, 1686, 1693-1699, 1703-1708, 1719-1733, 1780, 1813-1816, 1855-1857, 1930-1931, 1934-1935, 1940, 1949, 1955, 1960-1961, 1981-1986, 1990-2178 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_process.py 1969 1257 36% 40, 47-59, 75, 84-86, 89, 98, 104, 139, 143, 149-150, 162-174, 211, 232, 253-257, 280-308, 325-326, 331, 334-335, 338, 350, 386-387, 392-393, 401, 410-411, 415, 427-556, 560-628, 637, 651-652, 663-664, 669, 697, 702, 710, 724, 729, 736, 745, 749, 755-761, 764, 803-807, 816, 819-823, 827, 830, 832-845, 855-889, 917-918, 924, 927-929, 937-943, 970, 982, 992-1000, 1005, 1009, 1016-1026, 1032-1077, 1098, 1103-1106, 1109-1111, 1118-1137, 1161, 1163, 1166-1187, 1196-1272, 1279-1466, 1470-1499, 1503-1579, 1586-1674, 1678-1855, 1867, 1914, 1921-1924, 1931-1933, 1936, 1967-1968, 1972, 1977-1987, 1994-1995, 2016-2017, 2031-2078, 2133, 2166-2169, 2214-2216, 2223-2237, 2240-2249, 2254-2255, 2258-2267, 2270-2291, 2293-2294, 2358-2370, 2374-2419, 2441-2442, 2457, 2459, 2461, 2463, 2469, 2477, 2480, 2496, 2507, 2511, 2516-2620, 2627-2815, 3023-3034, 3039, 3042-3043, 3048-3050, 3053, 3077-3079, 3088, 3099-3111, 3122-3123, 3136, 3166, 3182, 3187-3194, 3453-3533, 3537-3576 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_send_or_copy.py 554 378 32% 19-195, 200-268, 273-332, 336-379, 397, 415, 424, 427-428, 430, 437, 444-449, 456, 462, 485-486, 493-623, 665-666, 671, 675-676, 680, 689-692, 700-716, 719-720, 728-741, 749, 751-754, 770, 809, 814-816, 833-834, 839 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_sort.py 193 163 16% 12-115, 125-127, 143-144, 161, 178-183, 189-287, 291-305 /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_util.py 298 134 55% 16-17, 21, 26, 40, 47-48, 70, 97-98, 106, 125-126, 143-155, 179-183, 194-196, 198-201, 214, 219, 224-231, 241-257, 261-284, 289, 293-294, 297-300, 303-305, 307-309, 319-324, 327-333, 366-371, 395, 405, 409-411, 423-467 /home/admin/workarea/git/Velours/python/mtr/datou/merge_rubbia.py 50 46 8% 12-36, 40-86 /home/admin/workarea/git/Velours/python/mtr/datou/send_mail_dechet.py 227 129 43% 19, 21, 27-28, 60, 66-119, 126-131, 146, 155, 157, 164-170, 175-179, 183-188, 191-193, 195-197, 207-221, 230, 234, 249-251, 255-259, 263, 269, 282-340, 344-345, 350, 354-355 /home/admin/workarea/git/Velours/python/mtr/lib/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/lib/fotonower_api/fotonower_connect.py 322 216 33% 73, 78-85, 88-90, 96-119, 123-184, 187-213, 220-221, 225, 230-231, 233-240, 252-253, 258-261, 267-279, 282, 285, 288-290, 307, 318, 323-325, 329, 332-335, 338-384, 389-412, 415-433, 436-461 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_detection.py 299 238 20% 35-43, 49-298, 304-342, 359, 373-374, 383-387, 402, 423-429, 444-549 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/mask_segment.py 67 16 76% 40, 87, 107-117, 173, 190-191, 196-197, 222 /home/admin/workarea/git/Velours/python/mtr/mask_rcnn/prepare_maskdata.py 359 81 77% 28, 51, 62, 78, 83, 120-121, 140-141, 145-146, 148-149, 154-155, 164, 176-177, 187, 195, 198, 202, 218-220, 241-243, 246, 269-270, 290-293, 315, 317-318, 321, 326-332, 335-342, 347-348, 360-369, 391-393, 400-403, 411-414, 424-427, 429, 460-462, 502-504, 513-514, 545 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/svm_subroutines.py 69 63 9% 21-43, 50-99, 104-136 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/__init__.py 1 0 100% /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/class_split_time_score.py 506 157 69% 48-49, 54, 73, 77, 85, 96, 114, 138-139, 145-146, 155, 237, 261, 267, 270, 283-292, 295, 298, 322, 325, 328, 331-342, 352-356, 367, 380-385, 412, 442, 444, 486, 498, 501, 513, 561-566, 570, 579, 595-599, 604-610, 615-621, 626-642, 645, 648, 651-667, 670-673, 676-679, 682-685, 688-695, 698-732 /home/admin/workarea/git/Velours/python/mtr/math_fotonower/timeseries/lib_split_time_score.py 1943 930 52% 19-36, 42, 78, 84-85, 96-104, 108-115, 125-136, 139-141, 182-185, 207-231, 236-411, 416-647, 654-729, 745-747, 752-776, 784-864, 868-886, 897-1001, 1006-1088, 1103, 1108, 1115, 1121-1134, 1139-1206, 1209-1250, 1255-1264, 1267-1351, 1354-1372, 1377-1414, 1419-1443, 1448-1467, 1695-1700, 1703-1721, 1768-1772, 1821, 1844-1864, 1948-1949, 1999, 2028, 2050-2105, 2175, 2178-2182, 2194, 2217, 2230, 2251-2252, 2271-2275, 2357, 2423, 2428, 2454-2458, 2544-2546, 2581-2582, 2613-2614, 2628-2630, 2647-2674, 2756-2757, 2774, 2821-2823, 2840-2842, 2876-2877, 2885-2898, 2913-2914, 2921, 2943-2944, 2951, 2959-2963, 2969-2973, 2983, 2996-3000, 3011, 3055-3106, 3114-3122, 3149-3151, 3170, 3176-3177, 3186-3188, 3190, 3217-3219, 3225, 3237-3238, 3246-3267, 3274, 3320, 3326-3395, 3404-3462, 3493-3494, 3540-3542, 3576, 3631, 3644, 3677-3722, 3754-3760, 3828-3829, 3854, 3860-3861, 3867, 3880-3881, 3895, 3902, 3917-3935, 3945-3963, 3970, 3974-4008 /home/admin/workarea/git/Velours/python/mtr/mem_info.py 76 30 61% 33-34, 41, 49, 59-63, 72, 95-124 /home/admin/workarea/git/Velours/python/mtr/monitor_sys.py 131 53 60% 40, 44, 47-50, 52, 61, 65-68, 98, 102, 104, 108, 110, 112, 114, 116, 124, 131, 143-144, 150, 162, 164-167, 170-194 /home/admin/workarea/git/Velours/python/mtr/ses_mailer.py 55 34 38% 35-36, 42-44, 47-85 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/flip_images.py 241 88 63% 20-91, 98-105, 111-114, 138-139, 155, 164-171, 177-180, 204-205, 220, 236, 243, 246, 253-254, 263, 268, 306-307, 311, 333, 338, 349, 378 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/image_utils.py 328 225 31% 21-28, 37-52, 88, 91-113, 121, 129, 142, 144-162, 181-191, 194-236, 242-253, 265-298, 301-314, 343, 348-354, 363-365, 368-381, 385-397, 401-441, 446-465, 470-473, 476-484 /home/admin/workarea/git/Velours/python/mtr/simple_image_editor/rotate_crop_and_images.py 894 306 66% 62, 65, 80-81, 97, 120, 139-140, 150-151, 176, 208, 212, 217-218, 223, 227-230, 239-241, 262, 268, 275, 297-310, 318-319, 333-334, 359-360, 370, 400-401, 412-413, 442, 457, 460, 481-482, 494, 497-498, 501, 521-524, 528, 531, 534, 543-546, 553-554, 571-572, 579-580, 584, 603, 606, 609, 618, 624, 634, 679-681, 690, 712-725, 733, 751, 794-831, 835, 884, 904-906, 911, 913-914, 920-924, 957-961, 964-967, 989, 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3200-3304, 3339, 3359-3360, 3362-3363, 3387, 3409-3417, 3508-3540, 3562, 3579-3590, 3594-3600, 3603-3682, 3685-3688, 3691-3723, 3726-3752, 3757-3819, 3825-3877 /home/admin/workarea/git/Velours/python/mtr/split_time_gps_score.py 723 514 29% 14-26, 36-68, 78-89, 106-107, 119-141, 151-182, 232, 236, 244-256, 299-300, 311, 314, 360-379, 399-401, 451-484, 488-509, 526-554, 558-596, 614, 631-634, 646, 649-650, 658, 665-671, 691-700, 713-721, 732-799, 812-865, 869-877, 881-893, 903-938, 948-987, 999-1034, 1046-1070, 1079, 1092-1093, 1097-1310 /home/admin/workarea/git/Velours/python/mtr/tfhub2/data_ops.py 228 196 14% 18-26, 29-37, 41-49, 52-60, 63-68, 71-82, 85-93, 96-117, 120-129, 132-149, 152-183, 187-189, 193-211, 215-229, 232-249, 271-302 /home/admin/workarea/git/Velours/python/mtr/tfhub2/evaluate.py 162 51 69% 42, 44-52, 86, 99, 110-111, 115, 128-131, 139, 173-181, 186-231, 246, 281-282, 293, 295 /home/admin/workarea/git/Velours/python/mtr/tfhub2/foto_datasets.py 242 131 46% 23-25, 41-51, 64-66, 82, 85, 95-108, 117-119, 122-123, 131, 136-137, 139-140, 153-168, 173-240, 246, 251, 256, 264, 271, 287-322, 341, 343, 366, 379-381, 386, 391, 395-401 /home/admin/workarea/git/Velours/python/mtr/tfhub2/fotonower_data_ops.py 111 94 15% 19-23, 26-30, 33-38, 41-44, 48-84, 91-144, 148-182, 186-192 /home/admin/workarea/git/Velours/python/mtr/tfhub2/ops.py 201 170 15% 29-31, 40, 44, 48, 60, 65-72, 76-129, 139-151, 155-167, 171-177, 182-186, 191-202, 207-210, 219-244, 254-280, 290-319, 324-326, 334-343, 346-348, 351-358, 362-373, 377-394 /home/admin/workarea/git/Velours/python/mtr/utils/MTRMongoClient.py 99 87 12% 21-92, 97-208, 213-241 /home/admin/workarea/git/Velours/python/mtr/utils/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cd.py 11 0 100% /home/admin/workarea/git/Velours/python/mtr/utils/cdn/copy_to_ovh.py 14 2 86% 16, 27 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/s3_bucket_manager.py 112 88 21% 33-40, 43-48, 51-54, 57-69, 75-84, 97-104, 119-125, 128-132, 140-159, 162-166, 169-175, 179-182, 185-186, 189-190 /home/admin/workarea/git/Velours/python/mtr/utils/cdn/swift_upload_manager.py 151 73 52% 44, 47-48, 54, 63, 72-73, 76-79, 105, 107, 125-127, 130, 133-142, 145-156, 163, 180-184, 187-193, 197-210, 213, 216-217, 223-239 /home/admin/workarea/git/Velours/python/mtr/utils/general_util.py 57 32 44% 11-12, 20-27, 30, 33-57, 61-63, 69-70, 75, 86-90 /home/admin/workarea/git/Velours/python/mtr/utils/kmean_cloud_storage.py 15 5 67% 19-20, 23, 26, 29 /home/admin/workarea/git/Velours/python/mtr/utils/load_caffe.py 61 26 57% 23, 29, 43, 48, 55, 61-62, 65, 70, 76-94 /home/admin/workarea/git/Velours/python/mtr/utils/prepare_photo_learning.py 201 125 38% 14-15, 62-81, 89, 94-97, 103, 114, 123-124, 131, 137, 140, 154, 156-158, 176, 189-232, 238-365 /home/admin/workarea/git/Velours/python/mtr/utils/upload_batch.py 58 19 67% 37-39, 46-47, 55-58, 68-78 /home/admin/workarea/git/Velours/python/mtr/utils/utils_timer.py 11 3 73% 13-15 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/home/admin/workarea/git/Velours/python/prod/memo/SLA_RUBBIA.py 546 200 63% 13-14, 115-116, 125-135, 149-164, 185-188, 196-223, 227, 229-230, 234-238, 257-258, 267-268, 270-273, 276, 287-295, 308-310, 317-320, 363, 383-427, 444-450, 452-453, 460-470, 484, 492, 505, 515-517, 525-527, 536, 540, 543-547, 549-550, 553, 561, 568-573, 620-622, 625-628, 643-646, 661-662, 666-667, 671-672, 675, 681-683, 693-695, 719, 725, 727-728, 734, 745, 749, 760-761, 774 /home/admin/workarea/git/Velours/python/prod/memo/__init__.py 0 0 100% /home/admin/workarea/git/Velours/python/prod/memo/example_of_unwanted_materials.py 90 58 36% 13-15, 43-62, 86-100, 104-126, 130-163, 167-170, 174-179 /home/admin/workarea/git/Velours/python/prod/memo/lib_sla.py 333 75 77% 19, 49, 68, 75, 79, 99-115, 132, 135, 185, 194-195, 230-231, 234, 240, 249-250, 257-258, 261-262, 280-282, 286-288, 293, 306-308, 368, 376-381, 385-399, 419, 453, 487, 504, 521-525, 544-547, 550-554, 585, 596-601 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249-250, 268, 276, 280-281, 288 /home/admin/workarea/git/Velours/python/tests/python_tests.py 221 46 79% 37-39, 94-95, 99, 105, 107, 112, 124, 128, 130, 134, 141, 143, 147, 149, 151, 153, 155, 157, 163, 165, 167, 172, 188, 202, 208, 222-225, 247-254, 271-272, 288-289, 370, 377 /home/admin/workarea/git/raspi-fotonower-x/python/__init__.py 0 0 100% /home/admin/workarea/git/raspi-fotonower-x/python/lib/__init__.py 0 0 100% /home/admin/workarea/git/raspi-fotonower-x/python/lib/conn_sqlite.py 669 587 12% 22-34, 37-51, 54-67, 71-86, 93-113, 120-130, 133-134, 137-138, 141-147, 150-153, 156-160, 165-179, 182-198, 201-220, 223-225, 228-236, 240-247, 254-268, 272-273, 276-281, 285-291, 299-339, 342-355, 361-392, 404-419, 426-457, 461-462, 465-467, 473-481, 489-495, 499-505, 508-519, 522-533, 536-542, 546-553, 556-623, 626-647, 650-651, 654-659, 662-679, 682-704, 707-713, 716-726, 730, 735-750, 754-766, 769-780, 783-796, 799-809, 815-833, 837-848, 852-866, 869-873, 876-884, 889, 892-893, 896-898, 902-904, 915-917, 921-976, 980-990, 993-1007, 1011-1013 /home/admin/workarea/git/raspi-fotonower-x/python/to_upload_status.py 2 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/__init__.py 8 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/classifier.py 40 20 50% 36, 44, 64-98 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/detector.py 98 88 10% 38-54, 72-99, 115-123, 140-179, 191-216 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/io.py 177 101 43% 9-14, 24-34, 41-46, 53-55, 61-63, 71-81, 88-92, 119, 151, 161, 168-185, 200, 218, 251, 255-263, 279-280, 307-309, 311, 339-346, 364-392 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/net_spec.py 131 95 27% 47-53, 64-79, 87-88, 94, 97, 105-119, 122-127, 130-133, 136-164, 174, 177, 180, 183, 186, 189-196, 205-213, 222-225 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/proto/caffe_pb2.py 506 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/caffe-fast-rcnn/python/caffe/pycaffe.py 156 61 61% 41-44, 52-54, 64-69, 110, 115-116, 123, 128, 154-182, 234-258, 266-269, 322-328 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/__init__.py 0 0 100% /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/bbox_transform.py 44 15 66% 11-28, 32 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/config.py 111 43 61% 218-223, 229-255, 259-263, 267-285 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/nms_wrapper.py 9 2 78% 16, 20 /home/admin/workarea/install/caffe_frcnn_python3/py-faster-rcnn/lib/fast_rcnn/test.py 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/usr/lib/python3/dist-packages/chardet/eucjpprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/euckrfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euckrprober.py 16 6 62% 36-39, 43, 47 /usr/lib/python3/dist-packages/chardet/euctwfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/euctwprober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/gb2312freq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/gb2312prober.py 16 6 62% 35-38, 42, 46 /usr/lib/python3/dist-packages/chardet/hebrewprober.py 77 48 38% 155-162, 165-171, 175-176, 179, 193, 223-253, 259-280, 284, 289-292 /usr/lib/python3/dist-packages/chardet/jisfreq.py 3 0 100% /usr/lib/python3/dist-packages/chardet/jpcntx.py 81 61 25% 124-129, 132-141, 144-168, 171, 175-178, 181, 185-186, 190, 193-210, 214-231 /usr/lib/python3/dist-packages/chardet/langbulgarianmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langcyrillicmodel.py 13 0 100% /usr/lib/python3/dist-packages/chardet/langgreekmodel.py 5 0 100% /usr/lib/python3/dist-packages/chardet/langhebrewmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langthaimodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/langturkishmodel.py 3 0 100% /usr/lib/python3/dist-packages/chardet/latin1prober.py 52 29 44% 98-101, 104-106, 110, 114, 117-128, 131-145 /usr/lib/python3/dist-packages/chardet/mbcharsetprober.py 44 33 25% 40-43, 46-51, 55, 59, 62-88, 91 /usr/lib/python3/dist-packages/chardet/mbcsgroupprober.py 14 3 79% 43-54 /usr/lib/python3/dist-packages/chardet/mbcssm.py 41 0 100% /usr/lib/python3/dist-packages/chardet/sbcharsetprober.py 75 60 20% 40-51, 54-61, 65-68, 72-75, 78-122, 125-132 /usr/lib/python3/dist-packages/chardet/sbcsgroupprober.py 19 8 58% 45-73 /usr/lib/python3/dist-packages/chardet/sjisprober.py 49 34 31% 38-42, 45-46, 50, 54, 57-87, 90-92 /usr/lib/python3/dist-packages/chardet/universaldetector.py 124 104 16% 82-92, 100-109, 125-218, 229-286 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298-302, 311, 323-326, 337-345 /usr/lib/python3/dist-packages/keystoneclient/v3/regions.py 20 5 75% 61, 75, 87, 112, 129 /usr/lib/python3/dist-packages/keystoneclient/v3/registered_limits.py 22 10 55% 61-72, 99, 120, 140, 157-158 /usr/lib/python3/dist-packages/keystoneclient/v3/role_assignments.py 69 48 30% 40-42, 45-47, 50-52, 55-57, 60-62, 95-124, 127, 131, 135, 139, 143, 147 /usr/lib/python3/dist-packages/keystoneclient/v3/roles.py 149 101 32% 61-92, 95-113, 116-121, 137-141, 156, 190-203, 217, 235, 275-284, 327-336, 377-386, 395, 401, 407, 413, 419, 430-433, 456-458, 481-482, 501-503, 521-523, 543-544, 559, 562, 566, 570 /usr/lib/python3/dist-packages/keystoneclient/v3/services.py 23 11 52% 57-58, 75, 89-90, 111-112, 130-134 /usr/lib/python3/dist-packages/keystoneclient/v3/tokens.py 37 27 27% 18-21, 37-39, 54-58, 78-94, 116-121 /usr/lib/python3/dist-packages/keystoneclient/v3/users.py 57 34 40% 43-45, 82-92, 126-132, 148, 187-197, 213-226, 240-243, 259-262, 278-281, 295 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47, 54, 60, 69-72, 81-84, 93-96, 105-108, 117-120, 129-132, 136, 140-143, 151-154, 162-174, 181-184, 191-199, 206, 213, 222-223, 228, 262-266, 275, 278, 285-293, 305, 316-319, 323, 330-339, 348-371, 377-378, 384-385, 396-400, 411-415, 426-429, 442-445, 456-459, 468, 472, 480, 485-487, 492, 500, 508, 513, 521, 530, 535, 544-557, 570-586, 596-600, 609, 618, 627, 636, 645, 651, 657, 661, 676-678, 685, 693-697, 705-734, 743-752, 760, 768-776, 782, 787-788, 796-798, 804-816, 820-823, 827-828, 906-908, 911-913, 915-917, 919-921, 924, 934-935, 938, 946, 953-968, 972-977, 989, 994, 999-1002, 1010, 1018-1019, 1024-1025, 1030, 1035-1036, 1041, 1049, 1064-1072, 1085-1093, 1102-1123, 1129, 1135-1138, 1146-1161, 1174-1193, 1202-1205, 1214-1217, 1229-1240, 1255-1281, 1297-1318, 1322-1323, 1327, 1361, 1365, 1371-1375, 1378-1397, 1402, 1407, 1413, 1419-1420, 1427, 1431, 1435, 1446-1448, 1477-1531, 1549-1576, 1589-1591, 1606-1650, 1663-1684, 1701-1730, 1746-1767, 1782-1796, 1811-1823, 1838-1852 /usr/lib/python3/dist-packages/netaddr/ip/glob.py 137 117 15% 26-67, 79-97, 109-127, 141-201, 213, 225-231, 283-285, 289, 293-294, 297, 300-301, 308, 312 /usr/lib/python3/dist-packages/netaddr/ip/nmap.py 64 55 14% 22-45, 51-62, 69-87, 96-101, 115-117 /usr/lib/python3/dist-packages/netaddr/ip/rfc1924.py 28 18 36% 32-42, 49-61 /usr/lib/python3/dist-packages/netaddr/ip/sets.py 350 300 14% 27-53, 65-81, 105-122, 126, 133, 145-210, 216-217, 226, 238-245, 249, 257, 263, 281-296, 317-350, 361, 371-372, 376-378, 392-413, 417, 426-429, 438-441, 450-453, 462-465, 476-479, 488-494, 505-507, 518-551, 566-619, 631-673, 683-688, 696, 700, 711-718, 729-735, 744-748 /usr/lib/python3/dist-packages/netaddr/strategy/__init__.py 113 90 20% 44-56, 70-83, 97-106, 121-138, 154-160, 177-194, 207-226, 238-257, 270-273 /usr/lib/python3/dist-packages/netaddr/strategy/eui48.py 135 70 48% 144-152, 163-197, 209-216, 226, 237-245, 249-251, 255-257, 261-263, 267-269, 273-275, 279-281, 286-288, 292, 296 /usr/lib/python3/dist-packages/netaddr/strategy/eui64.py 122 66 46% 121-124, 133-139, 149-176, 187-192, 202-203, 214-222, 226-228, 232-234, 238-240, 244-246, 250-252, 256-258, 263-265, 269, 273 /usr/lib/python3/dist-packages/netaddr/strategy/ipv4.py 103 51 50% 16, 91-107, 121, 141-148, 158-161, 171, 182, 186, 196-199, 212-214, 218, 222, 226-228, 232, 236, 240, 261-278 /usr/lib/python3/dist-packages/netaddr/strategy/ipv6.py 106 47 56% 18, 24-25, 119-126, 141-142, 154-172, 182-187, 197-198, 221, 225-229, 233, 237, 241, 245-247, 251, 255, 259 /usr/lib/python3/dist-packages/oauthlib/__init__.py 10 2 80% 27, 34 /usr/lib/python3/dist-packages/oauthlib/common.py 212 144 32% 22-24, 27-28, 59, 64-70, 74-80, 84-89, 96-101, 108-113, 129-165, 176-194, 209, 221, 232-233, 237-251, 255-257, 266, 271-275, 280-285, 297-303, 308-328, 338-340, 343, 346-348, 351-352, 355, 358-359, 362-364, 385-430, 433-436, 439-447, 452, 456-458, 463-468 /usr/lib/python3/dist-packages/oauthlib/oauth1/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/__init__.py 125 91 27% 51, 86-101, 104-110, 122-151, 156-186, 199-223, 256-327 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/__init__.py 8 0 100% /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/access_token.py 59 48 19% 44-54, 104-119, 131-217 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/authorization.py 38 25 34% 50-57, 111-139, 154-163 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/base.py 87 75 14% 23-24, 32-66, 70-106, 110-112, 117-177, 182-216 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/pre_configured.py 8 4 50% 11-14 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/request_token.py 56 45 20% 42-49, 99-110, 122-211 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/resource.py 43 35 19% 70-165 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/endpoints/signature_only.py 34 26 24% 35-84 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/errors.py 36 15 58% 39-46, 49, 53-58, 62 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/parameters.py 36 23 36% 48-91, 105-112, 124, 136-139 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/request_validator.py 108 48 56% 116, 120, 124, 128, 132, 136, 140, 144, 148, 152, 156, 162-163, 170-171, 178-179, 186-187, 194-195, 200, 207-208, 232, 248, 264, 300, 333, 366, 383, 398, 416, 440, 467, 504, 539, 574, 625, 659, 678, 713, 745, 764, 788, 812, 833, 854 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/signature.py 149 117 21% 84-106, 131-205, 279-340, 423-438, 442, 468-495, 499, 525-552, 559-562, 580-586, 590-592, 616-627, 631, 651-661, 681-691, 695-697, 716-729, 739-743 /usr/lib/python3/dist-packages/oauthlib/oauth1/rfc5849/utils.py 40 22 45% 31-32, 40-44, 55-60, 64-66, 72, 78, 83-90 /usr/lib/python3/dist-packages/os_service_types/__init__.py 9 3 67% 39-41 /usr/lib/python3/dist-packages/os_service_types/data/__init__.py 8 0 100% /usr/lib/python3/dist-packages/os_service_types/exc.py 10 2 80% 24-25 /usr/lib/python3/dist-packages/os_service_types/service_types.py 111 69 38% 28-29, 59, 64-71, 75-78, 83, 88, 93, 98, 103, 108, 114, 120, 129-133, 141-144, 152, 160-161, 169, 191-202, 210-211, 221-234, 242-245, 254-258, 269-270, 281-285 /usr/lib/python3/dist-packages/oslo_config/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_config/cfg.py 1258 889 29% 38-39, 77, 80, 87, 94-97, 104-105, 108-109, 116, 119, 126, 129, 136-137, 140-141, 149, 156, 159, 167, 170, 178, 181, 188-189, 192, 211, 237-248, 262-265, 269-278, 309, 339, 355-357, 375-388, 392-395, 400-402, 414-423, 541, 545, 548, 559, 574, 584-585, 589-592, 603-604, 612-613, 623-632, 635, 638, 648-676, 688-697, 713-729, 738-741, 753-763, 776-779, 797-806, 809, 869-870, 873, 876, 879, 930-934, 938-949, 966-968, 972-973, 977-988, 994-1006, 1026, 1047, 1086, 1104, 1134-1136, 1152, 1173, 1199-1200, 1229, 1233-1238, 1253, 1291-1295, 1299-1312, 1346-1351, 1354, 1358-1360, 1395-1406, 1409, 1414-1416, 1461-1469, 1478, 1482, 1497-1502, 1509-1510, 1513-1517, 1521, 1524, 1529-1530, 1533, 1548-1552, 1555, 1558-1559, 1562-1566, 1570-1581, 1584, 1587, 1596-1617, 1640-1647, 1672-1697, 1707-1710, 1717, 1724, 1737-1747, 1758-1788, 1802-1807, 1831-1858, 1861-1863, 1878-1879, 1882-1886, 1894-1910, 1913-1914, 1917-1918, 1921-1922, 1979-1999, 2003, 2031-2034, 2039-2049, 2059, 2066-2071, 2120-2141, 2145-2148, 2151-2187, 2196-2201, 2205, 2209, 2213-2215, 2219, 2223-2224, 2236-2244, 2247-2252, 2257-2269, 2289-2293, 2300, 2304, 2307, 2316-2317, 2332-2335, 2340-2341, 2351-2354, 2364-2380, 2385-2386, 2404-2405, 2422-2423, 2438-2441, 2459-2462, 2468-2471, 2484-2485, 2497-2498, 2502-2506, 2510-2511, 2515-2517, 2521-2523, 2542-2554, 2567-2591, 2603-2605, 2617-2619, 2622-2633, 2646-2735, 2749-2768, 2783-2786, 2806-2815, 2818-2830, 2839-2863, 2871-2879, 2893-2897, 2906-2937, 2940-2955, 2958-2965, 2975-2987, 2996, 3009-3032, 3040-3054, 3065-3081, 3089-3094, 3112-3114, 3129-3130, 3134, 3138, 3142, 3146-3147, 3151, 3167-3169, 3173-3185, 3202-3204, 3212-3229 /usr/lib/python3/dist-packages/oslo_config/iniparser.py 75 57 24% 18-20, 23, 31-32, 35-40, 43-56, 59-97, 101, 105, 109, 112, 116, 119, 123, 127 /usr/lib/python3/dist-packages/oslo_config/sources/__init__.py 11 0 100% /usr/lib/python3/dist-packages/oslo_config/sources/_environment.py 22 11 50% 70, 73, 81-82, 85-92 /usr/lib/python3/dist-packages/oslo_config/types.py 419 300 28% 44-53, 56-61, 65, 113, 120-123, 129, 133-139, 142-167, 173-180, 183, 194, 201, 209-215, 218, 238, 241-250, 253, 256, 259, 282-304, 307-324, 327-338, 341, 352, 388, 411, 437-446, 479, 487-519, 522, 525, 531-540, 562-565, 568-586, 589, 596, 618-626, 629-680, 683, 686, 692-694, 713-722, 725-729, 732, 735, 738-739, 742-743, 746-748, 751, 766, 785-799, 802, 805, 808, 829-831, 841-848, 851, 854, 857, 880-882, 885-906, 910, 914, 918, 921, 924-932, 935 /usr/lib/python3/dist-packages/oslo_i18n/__init__.py 4 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_factory.py 75 32 57% 83, 99-121, 136-152, 169, 182, 185, 190, 195, 200, 205 /usr/lib/python3/dist-packages/oslo_i18n/_gettextutils.py 41 20 51% 48-50, 61-101 /usr/lib/python3/dist-packages/oslo_i18n/_lazy.py 4 1 75% 38 /usr/lib/python3/dist-packages/oslo_i18n/_locale.py 2 0 100% /usr/lib/python3/dist-packages/oslo_i18n/_message.py 95 71 25% 59-69, 94-104, 115-132, 137-179, 194-215, 221-227, 238-251, 254-259, 262-264, 267 /usr/lib/python3/dist-packages/oslo_i18n/_translate.py 17 13 24% 39-49, 67-73 /usr/lib/python3/dist-packages/oslo_log/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_serialization/jsonutils.py 82 52 37% 85-181, 217, 235-236, 248, 260, 268-270 /usr/lib/python3/dist-packages/oslo_utils/__init__.py 0 0 100% /usr/lib/python3/dist-packages/oslo_utils/_i18n.py 4 0 100% /usr/lib/python3/dist-packages/oslo_utils/encodeutils.py 60 53 12% 38-63, 84-104, 114-119, 135-188 /usr/lib/python3/dist-packages/oslo_utils/importutils.py 40 24 40% 29-34, 44, 60-65, 92-97, 117-122 /usr/lib/python3/dist-packages/oslo_utils/reflection.py 107 83 22% 44-47, 55-58, 63, 78-96, 107-111, 121-153, 158-163, 168-186, 191, 196, 208-214, 219-220 /usr/lib/python3/dist-packages/oslo_utils/strutils.py 183 135 26% 127, 146-165, 177-178, 214-247, 265-272, 333-359, 416-441, 453-456, 470-488, 503-520, 545-569, 579-586 /usr/lib/python3/dist-packages/oslo_utils/timeutils.py 230 155 33% 35, 56-64, 71-74, 95-97, 102, 109, 120-125, 135-140, 151-165, 177-180, 182, 201, 217, 226-231, 240, 249, 258-267, 283-297, 306-307, 318-319, 333-334, 339, 344, 347-349, 379-396, 421-428, 435-441, 446, 450-459, 465-468, 473, 477-486, 490-491, 495-498, 508-516, 520-525, 529, 533, 537-541, 546-553 /usr/lib/python3/dist-packages/paramiko/__init__.py 34 0 100% /usr/lib/python3/dist-packages/paramiko/_version.py 2 0 100% /usr/lib/python3/dist-packages/paramiko/agent.py 223 152 32% 64-72, 75-78, 81-85, 88-96, 105-107, 110-126, 129-150, 153-155, 165, 173-180, 189-190, 193, 210-213, 216, 222-241, 248-252, 263-269, 272, 275-279, 286-290, 299, 302, 328-331, 334, 337, 340-341, 363-368, 370-375, 379, 385, 396-399, 402, 405, 408, 411-419 /usr/lib/python3/dist-packages/paramiko/auth_handler.py 521 373 28% 105-108, 111-118, 132-140, 146-155, 158-167, 170-177, 180-181, 192-198, 201-207, 219-220, 233-236, 239-240, 249, 254-270, 282-284, 290-291, 300-397, 402, 408-428, 432-441, 444-615, 630-632, 634-641, 654-656, 660-678, 681-694, 699-706, 737, 752-753, 756-757, 761, 765, 769, 773, 776, 779-802, 805-826, 829-830, 833-834, 847 /usr/lib/python3/dist-packages/paramiko/ber.py 84 66 21% 34-35, 38, 41, 44, 47, 50-93, 97-104, 108-114, 117-129, 135-138 /usr/lib/python3/dist-packages/paramiko/buffered_pipe.py 93 45 52% 55-59, 64, 77-90, 99-106, 118-124, 155, 162-164, 171-174, 188-196, 208, 218-222 /usr/lib/python3/dist-packages/paramiko/channel.py 597 342 43% 71, 139-142, 148-161, 190-203, 223-230, 275-283, 300-309, 330-335, 355-362, 377, 402-404, 419-425, 474-492, 509-516, 522, 532, 538, 549, 572-584, 612, 632-635, 645, 654-671, 683, 700-701, 706-710, 727, 748-749, 754-758, 775-781, 798-801, 821-825, 845-848, 866-869, 933-944, 959, 967, 979, 997, 1032-1039, 1042-1047, 1050-1060, 1067, 1082-1155, 1157-1163, 1173, 1192-1209, 1223-1226, 1238, 1254, 1267-1274, 1281-1292, 1305-1333, 1358, 1364-1365, 1379-1380 /usr/lib/python3/dist-packages/paramiko/client.py 275 127 54% 100-108, 126-127, 142-148, 161, 170, 191, 215-216, 345-348, 352-360, 368, 385, 387, 389, 394, 401-404, 419-423, 426, 430-433, 459-466, 510, 513, 545-548, 556, 566, 580-581, 596-597, 632, 637-641, 648-653, 656-670, 674-687, 694-707, 727, 730, 745, 749-765, 789, 801, 817-823, 835 /usr/lib/python3/dist-packages/paramiko/common.py 93 4 96% 186-187, 207-211 /usr/lib/python3/dist-packages/paramiko/compress.py 12 4 67% 29, 32, 37, 40 /usr/lib/python3/dist-packages/paramiko/config.py 130 107 18% 50, 58-94, 126-147, 154-157, 160-166, 180-239, 245-248, 257-259, 262-303, 342, 358-361, 372 /usr/lib/python3/dist-packages/paramiko/dsskey.py 115 86 25% 55-81, 84-90, 93, 96, 99, 102, 105, 108-130, 133-159, 162-172, 180-190, 207-219, 224-225, 228-229, 234-247 /usr/lib/python3/dist-packages/paramiko/ecdsakey.py 158 107 32% 77, 80-82, 85-87, 90-92, 119-168, 172, 175-192, 195, 198, 207, 210, 213, 216-223, 226-239, 242, 250, 266-273, 278-279, 282-283, 286-296, 299-302, 305-308 /usr/lib/python3/dist-packages/paramiko/ed25519key.py 122 36 70% 42-47, 68, 79-80, 86, 102, 113-123, 126, 132, 139-154, 160, 165, 184, 198-202, 208, 221, 225-226 /usr/lib/python3/dist-packages/paramiko/file.py 250 156 38% 76-78, 98-111, 125-128, 138, 148, 158, 168-170, 189-229, 255, 257, 268-273, 277-283, 289, 292-293, 298-334, 352-355, 376, 386, 397-422, 433-435, 442, 446, 456, 463, 474, 492, 494-496, 506-508, 512-516, 522-529, 536-545 /usr/lib/python3/dist-packages/paramiko/hostkeys.py 200 138 31% 27, 63, 75-77, 95-110, 125-129, 144-146, 149-150, 153, 156-160, 163-166, 169-180, 183, 191-192, 195, 203-211, 223-229, 235, 238-239, 242, 248, 251-258, 262-273, 277-282, 285-288, 301-310, 315-317, 343-374, 382-388, 391 /usr/lib/python3/dist-packages/paramiko/kex_curve25519.py 82 29 65% 31-32, 39, 47-48, 62, 65, 70-100 /usr/lib/python3/dist-packages/paramiko/kex_ecdh_nist.py 87 59 32% 26-30, 33-47, 50-54, 59-63, 66-106, 109-137 /usr/lib/python3/dist-packages/paramiko/kex_gex.py 181 153 15% 61-68, 71-91, 94-105, 111-125, 128-163, 168-190, 193-210, 213-251, 254-282 /usr/lib/python3/dist-packages/paramiko/kex_group1.py 85 62 27% 51-54, 57-69, 72-77, 89-96, 100-121, 125-154 /usr/lib/python3/dist-packages/paramiko/kex_group14.py 11 0 100% /usr/lib/python3/dist-packages/paramiko/kex_group16.py 9 0 100% /usr/lib/python3/dist-packages/paramiko/kex_gss.py 345 294 15% 84-89, 95-110, 124-135, 148-153, 162-166, 175-189, 199-234, 243-288, 303-307, 342-351, 357-370, 379-394, 400-414, 423-458, 466-486, 499-551, 562-566, 574-588, 596-638, 651-655, 674, 677, 680 /usr/lib/python3/dist-packages/paramiko/message.py 104 24 77% 60, 86-89, 111, 123, 138-142, 156, 164, 241-246, 254-255, 291, 293, 295 /usr/lib/python3/dist-packages/paramiko/packet.py 372 118 68% 59-62, 132, 173-174, 207, 210, 213-214, 217, 220, 223, 226, 242-244, 247, 272, 300-302, 306, 310, 315-328, 331, 333, 344-359, 361-363, 371, 374, 377, 405, 410, 413-417, 421, 437, 451-457, 471-484, 489, 494, 503, 515, 527, 532, 540, 546, 558-566, 573-580, 586, 588, 597-603, 613-616, 624, 626-637, 655, 660 /usr/lib/python3/dist-packages/paramiko/pipe.py 84 60 29% 34-38, 43-46, 49-52, 55, 58-61, 64-67, 70-71, 81-93, 96-99, 102, 105-108, 111-114, 117-118, 123-125, 128-130, 133-135, 144-148 /usr/lib/python3/dist-packages/paramiko/pkey.py 183 97 47% 82, 90, 93, 107-111, 114, 124, 133, 140, 161, 171, 183, 227-228, 242, 255, 287, 289, 297-298, 307-308, 313-338, 362-367, 370-375, 402, 415-426, 447-457, 487-489, 496-498, 505-525, 535-536, 539-542, 546, 549 /usr/lib/python3/dist-packages/paramiko/primes.py 69 58 16% 32-49, 60-61, 64-107, 113-122, 125-148 /usr/lib/python3/dist-packages/paramiko/proxy.py 51 33 35% 53-59, 68-76, 86-109, 112, 116, 121, 124 /usr/lib/python3/dist-packages/paramiko/py3compat.py 101 59 42% 32-102, 124, 132-133, 148-151, 154, 162, 165 /usr/lib/python3/dist-packages/paramiko/rsakey.py 89 34 62% 52-53, 57-67, 73, 80, 94-99, 102, 110, 113, 126-139, 142, 150, 167-170, 179-180, 187-188 /usr/lib/python3/dist-packages/paramiko/server.py 98 60 39% 88, 105, 124, 149, 181, 206, 237, 265-267, 297-299, 310-311, 332, 343, 373, 398, 414, 433, 457-463, 483, 509, 522, 562, 580, 594, 614-621, 633, 665-669, 676, 679-695, 721, 730 /usr/lib/python3/dist-packages/paramiko/sftp.py 89 57 36% 126-128, 133-140, 145-155, 158, 161-168, 171-191, 194-198, 201-213 /usr/lib/python3/dist-packages/paramiko/sftp_attr.py 150 127 15% 54-61, 73-82, 85, 90-96, 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797-804, 814-834, 853-855, 871, 873, 893-898, 913, 915, 933, 937, 948-955, 976-977 /usr/lib/python3/dist-packages/stevedore/__init__.py 9 0 100% /usr/lib/python3/dist-packages/stevedore/driver.py 29 17 41% 51-53, 66, 100-105, 108-118, 139-141, 147-148 /usr/lib/python3/dist-packages/stevedore/enabled.py 13 7 46% 64-65, 77-84 /usr/lib/python3/dist-packages/stevedore/exception.py 3 0 100% /usr/lib/python3/dist-packages/stevedore/extension.py 104 71 32% 46-49, 58, 99-107, 141-146, 150-152, 155-156, 160-165, 176-179, 183, 187-214, 220-230, 237, 259-265, 269, 290, 294-301, 309, 317, 326, 331 /usr/lib/python3/dist-packages/stevedore/hook.py 11 6 45% 59, 74-78, 87-89 /usr/lib/python3/dist-packages/stevedore/named.py 34 24 29% 74-89, 123-129, 134-140, 143-146, 154-156 /usr/lib/python3/dist-packages/swiftclient/__init__.py 7 2 71% 31-32 /usr/lib/python3/dist-packages/swiftclient/client.py 959 551 43% 53-63, 71-72, 75-76, 89, 128-135, 146-156, 164-190, 195, 197-208, 214-217, 220-221, 232, 242, 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434-439, 448-456, 460-469, 473 /usr/lib/python3/dist-packages/urllib3/request.py 39 27 31% 54, 70-79, 88-97, 144-171 /usr/lib/python3/dist-packages/urllib3/response.py 399 219 45% 34-36, 39, 42-61, 73-74, 77, 80-98, 103-118, 131, 134, 137-139, 143-152, 190, 217, 236, 251, 258, 268-271, 283-287, 291, 294, 302, 315-322, 332, 337-338, 341, 347-348, 352, 365, 367-373, 377, 388-391, 397, 406-410, 427-443, 455-462, 495, 518-529, 539, 560-561, 585, 603, 610, 615, 618, 621, 626, 628, 631-634, 637-642, 648-653, 657, 661-666, 675, 680-689, 692-711, 727-781, 789-792, 795-809 /usr/lib/python3/dist-packages/urllib3/util/__init__.py 10 0 100% /usr/lib/python3/dist-packages/urllib3/util/connection.py 66 18 73% 19, 21, 25-26, 53, 73, 77-86, 91, 118, 130-131 /usr/lib/python3/dist-packages/urllib3/util/queue.py 14 1 93% 7 /usr/lib/python3/dist-packages/urllib3/util/request.py 50 22 56% 13, 63, 65, 71, 74, 77, 80, 85, 96, 98-103, 119-133 /usr/lib/python3/dist-packages/urllib3/util/response.py 35 17 51% 19-35, 55, 71, 83-86 /usr/lib/python3/dist-packages/urllib3/util/retry.py 150 95 37% 186-187, 202-218, 223-232, 240-249, 253-265, 270-275, 278-283, 286-289, 300-305, 311, 317, 339, 350-355, 376-442, 445 /usr/lib/python3/dist-packages/urllib3/util/ssl_.py 148 78 47% 31-34, 43-44, 50-56, 61-63, 104-149, 162-174, 193, 198, 201, 211-217, 332, 337-348, 354, 357-360, 372-383, 395, 401-407 /usr/lib/python3/dist-packages/urllib3/util/timeout.py 63 25 60% 102, 121, 126-153, 192, 204-208, 223-226, 252-254, 256 /usr/lib/python3/dist-packages/urllib3/util/url.py 205 82 60% 102, 112, 117-122, 127-129, 150-169, 172, 193-207, 239, 252, 258-259, 264, 269, 277, 282-294, 299, 304-314, 354, 358, 372, 374-376, 379-381, 391-394, 401-404, 411, 431-432 /usr/lib/python3/dist-packages/urllib3/util/wait.py 76 37 51% 8-9, 48-68, 72-87, 92, 97, 111, 121-122, 135-138, 153 /usr/lib/python3/dist-packages/yaml/__init__.py 184 118 36% 15-16, 31-37, 45, 62-67, 73-78, 85-89, 96-101, 123-132, 142, 152, 162, 172, 182, 192, 201-213, 224-243, 250, 262-283, 290, 298, 306, 316-322, 331-337, 345-350, 359-364, 373, 382, 391-397, 418, 425 /usr/lib/python3/dist-packages/yaml/composer.py 92 31 66% 18-22, 26-27, 40-41, 65-70, 74-75, 82, 96, 100-115, 126 /usr/lib/python3/dist-packages/yaml/constructor.py 479 295 38% 32, 38-39, 44-45, 52, 61, 69, 71-72, 74, 82-98, 102, 107-108, 114, 119, 125-129, 134, 141, 148-157, 175-177, 186-204, 208-209, 213, 221-222, 234-235, 242, 244, 246, 248, 250, 252, 254-261, 271-292, 295-307, 323-350, 356-373, 377-394, 397-400, 406-408, 417-424, 427, 487, 490-492, 495, 498, 501-513, 517, 520, 523, 526-538, 541-563, 566-570, 573-577, 581-593, 596-612, 617-621, 635-656, 659, 716, 719, 722, 726 /usr/lib/python3/dist-packages/yaml/cyaml.py 46 24 48% 19-21, 26-28, 33-35, 40-42, 47-49, 59-66, 76-83, 93-100 /usr/lib/python3/dist-packages/yaml/dumper.py 23 12 48% 17-25, 35-43, 53-61 /usr/lib/python3/dist-packages/yaml/emitter.py 838 769 8% 22-29, 42-104, 108-109, 112-116, 121-131, 134-144, 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/usr/local/lib/python3.8/dist-packages/IPython/__init__.py 32 12 62% 31, 90-98, 125-126, 151-152 /usr/local/lib/python3.8/dist-packages/IPython/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/alias.py 107 66 38% 71-88, 101-108, 130-134, 138-160, 163, 166-186, 199-202, 206-209, 213, 218-221, 229-230, 235-236, 240, 243-246, 249-250, 254-258 /usr/local/lib/python3.8/dist-packages/IPython/core/application.py 251 170 32% 35-39, 58-63, 91-100, 122, 125-126, 136, 145-152, 160, 174-180, 187-191, 203, 222-229, 238-243, 247-251, 260-263, 267-287, 308-350, 355-405, 409-433, 440-445, 450-462 /usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py 68 51 25% 26-28, 31, 40-42, 46-55, 67-73, 83-92, 102-105, 108-122, 133-138, 152-173 /usr/local/lib/python3.8/dist-packages/IPython/core/autocall.py 16 5 69% 40, 48, 57, 69-70 /usr/local/lib/python3.8/dist-packages/IPython/core/builtin_trap.py 48 32 33% 26-34, 40-44, 47-51, 55-63, 67-70, 75-77, 82-86 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/usr/local/lib/python3.8/dist-packages/IPython/core/getipython.py 5 1 80% 24 /usr/local/lib/python3.8/dist-packages/IPython/core/history.py 384 277 28% 13-17, 40, 43, 46, 49, 55-58, 65-70, 86-119, 125, 129, 132, 135, 191-198, 214-229, 242, 247-268, 273, 294-303, 330-331, 340-341, 362-369, 395-411, 441-448, 467-469, 488-491, 536-551, 558-559, 564-570, 574-579, 583-584, 590-599, 628-631, 636-651, 681-685, 707-739, 751-758, 761-763, 767-769, 775-802, 816-819, 824-836, 845-847, 868-899, 904-906 /usr/local/lib/python3.8/dist-packages/IPython/core/hooks.py 59 39 34% 66-82, 86, 97-100, 109-117, 120, 124-125, 132, 142, 155, 165, 170, 176-190 /usr/local/lib/python3.8/dist-packages/IPython/core/inputtransformer2.py 359 284 21% 27-32, 40-47, 74, 77-82, 92-99, 108-117, 124-129, 154-155, 182, 185-186, 199, 207, 215-221, 226-238, 247-260, 265-278, 302-312, 320-323, 330-333, 337-338, 342-343, 347-348, 352-353, 370-380, 385-405, 422-424, 430-437, 442-463, 478-503, 507-513, 525-534, 555-571, 574-579, 584-591, 611-714, 718-721 /usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py 1477 1139 23% 95-97, 116-117, 123-124, 139-163, 210-222, 238-248, 252, 260-262, 272-274, 295-298, 301-304, 320, 324, 328-331, 334-335, 388, 392-403, 455-456, 466-467, 475, 487, 501-505, 553-554, 609-611, 630-703, 707, 714, 720-723, 726, 733-737, 740-743, 747-771, 775, 781-784, 790-791, 795, 799-801, 804, 810-815, 824-825, 833-836, 841, 853-856, 861-863, 866-867, 870-871, 874-878, 882-890, 903-945, 956-961, 965-972, 980-985, 988-990, 998-1010, 1024-1056, 1063-1065, 1072-1074, 1080-1081, 1107-1123, 1145, 1154, 1157, 1161-1168, 1184-1191, 1235-1271, 1278, 1304-1322, 1341-1342, 1373-1400, 1409, 1419-1464, 1479-1509, 1521-1531, 1549-1579, 1594-1597, 1610-1691, 1708-1737, 1741, 1748-1764, 1768-1775, 1779, 1787-1794, 1802-1803, 1813-1833, 1875-1933, 1957, 1967-1989, 1996, 2003-2005, 2020-2059, 2067, 2081-2093, 2103, 2114-2116, 2127, 2131, 2145-2166, 2211-2212, 2220-2221, 2225-2230, 2237-2268, 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659-661, 675-683, 687-703 /usr/local/lib/python3.8/dist-packages/IPython/core/magic_arguments.py 102 16 84% 130, 135-136, 153, 164, 172, 190, 203-204, 206, 232, 262, 269, 272-274 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/IPython/core/magics/auto.py 38 27 29% 29-31, 51-60, 107-128 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/basic.py 249 188 24% 22-23, 27-38, 41, 44, 51-64, 67, 130-174, 181, 185-193, 210-275, 293-302, 307-309, 326-359, 369-379, 384-386, 415-465, 493-500, 544-546, 567-582, 622-651 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/code.py 304 260 14% 64-81, 100-134, 141-164, 170, 178-179, 209-243, 260-275, 285, 325-377, 383-507, 511-517, 664-730 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/config.py 46 36 22% 32-33, 107-158 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/display.py 29 10 66% 40, 45, 56, 61, 72-77, 82 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495-501, 507-511, 517, 536-555, 650-671, 715-725, 750-788, 805-815, 833-849 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/packaging.py 49 32 35% 22-23, 30-44, 68-69, 78-103 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/pylab.py 45 23 49% 94-100, 139-159, 165-166 /usr/local/lib/python3.8/dist-packages/IPython/core/magics/script.py 156 119 24% 89-104, 115-119, 122, 125-127, 132-153, 178-245, 249-253, 258-259, 263-291, 294 /usr/local/lib/python3.8/dist-packages/IPython/core/oinspect.py 489 415 15% 50, 86-88, 96-112, 124-132, 154-191, 196, 209-215, 227-231, 238-266, 277-285, 303-319, 338-352, 361-366, 373-376, 380, 384-386, 390-394, 401-415, 453-475, 481-490, 495-512, 531-550, 555-567, 586-656, 680-683, 688-692, 717-905, 915-922, 950-988, 997-1031 /usr/local/lib/python3.8/dist-packages/IPython/core/page.py 174 147 16% 37-43, 51, 62-78, 86-123, 152-234, 248-260, 267-280, 288-302, 311-318, 323-336, 339-343 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218, 222-256, 265-286, 290-305, 309-324, 327-354, 358-384, 388-397, 401-427, 431-440 /usr/local/lib/python3.8/dist-packages/IPython/core/splitinput.py 34 23 32% 58-78, 111-119, 134, 137 /usr/local/lib/python3.8/dist-packages/IPython/core/ultratb.py 709 610 14% 152, 170-234, 247-300, 311-319, 330-343, 348-374, 399-423, 426-435, 450-475, 488-509, 522, 525-526, 531-542, 545-552, 558-563, 568-574, 578, 585-587, 595, 619, 623-625, 628-631, 662-704, 719-741, 756-813, 823, 835-838, 842-845, 867-881, 885-894, 898-1069, 1074-1093, 1096-1106, 1116-1141, 1144-1165, 1171-1205, 1227-1248, 1251-1257, 1263-1270, 1293-1307, 1310-1324, 1330, 1338-1350, 1354, 1357, 1360, 1363, 1392-1403, 1407-1414, 1425, 1433-1434, 1437-1439, 1446-1454, 1459-1461, 1465, 1472-1495, 1499, 1503 /usr/local/lib/python3.8/dist-packages/IPython/core/usage.py 9 0 100% /usr/local/lib/python3.8/dist-packages/IPython/display.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/extensions/storemagic.py 114 98 14% 24-31, 35-46, 50, 54-56, 72-75, 121-227, 232 /usr/local/lib/python3.8/dist-packages/IPython/lib/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/IPython/lib/backgroundjobs.py 209 164 22% 76-89, 93-94, 98-99, 103-104, 171-197, 200-201, 209, 223-242, 249-254, 261-266, 276-282, 287-293, 298-310, 322-330, 334-337, 340-344, 347-354, 386, 391-417, 420, 423, 426, 429-442, 455-462, 465, 477-488, 491 /usr/local/lib/python3.8/dist-packages/IPython/lib/clipboard.py 41 33 20% 17-33, 38-44, 53-67 /usr/local/lib/python3.8/dist-packages/IPython/lib/display.py 254 202 20% 99-115, 119-128, 133-151, 155-173, 178-194, 198-200, 204-210, 213-219, 223-231, 234-237, 240-243, 261-264, 268-276, 308-310, 314-319, 327-328, 343-344, 381-387, 390-391, 399-405, 410, 471-488, 512-544, 550-566, 575-579, 584-592, 597-605, 627-628, 631-639, 642, 645-649, 652-654 /usr/local/lib/python3.8/dist-packages/IPython/lib/pretty.py 500 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/usr/local/lib/python3.8/dist-packages/IPython/terminal/ptutils.py 86 64 26% 38-54, 58-61, 67-70, 74-77, 80-92, 99-133, 140-144, 155-168 /usr/local/lib/python3.8/dist-packages/IPython/terminal/shortcuts.py 140 111 21% 27-28, 34-95, 99-104, 109-152, 161, 170, 174-176, 180-184, 188-191, 194, 200, 203, 215-226, 238-249, 253-254, 258-274 /usr/local/lib/python3.8/dist-packages/IPython/testing/__init__.py 9 5 44% 33-37 /usr/local/lib/python3.8/dist-packages/IPython/testing/skipdoctest.py 3 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/PyColorize.py 110 82 25% 143, 186-196, 200-205, 217-281, 286-325, 330 /usr/local/lib/python3.8/dist-packages/IPython/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_common.py 68 57 16% 34-40, 69-111, 130-133, 151, 171-175, 190-212 /usr/local/lib/python3.8/dist-packages/IPython/utils/_process_posix.py 82 57 30% 37-39, 62-67, 94-97, 115-118, 133-203, 214-224 /usr/local/lib/python3.8/dist-packages/IPython/utils/_sysinfo.py 1 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/capture.py 103 70 32% 18-21, 24-25, 29-35, 38, 41, 44, 47, 50, 53, 56, 59, 76-80, 83, 88-90, 95-97, 110, 114-119, 131-134, 137-163, 166-170 /usr/local/lib/python3.8/dist-packages/IPython/utils/colorable.py 7 0 100% /usr/local/lib/python3.8/dist-packages/IPython/utils/coloransi.py 67 12 82% 93-94, 122-124, 149, 156, 161, 172-173, 179-180 /usr/local/lib/python3.8/dist-packages/IPython/utils/contexts.py 26 16 38% 37-38, 42-53, 56-60, 70 /usr/local/lib/python3.8/dist-packages/IPython/utils/data.py 6 3 50% 22-23, 28 /usr/local/lib/python3.8/dist-packages/IPython/utils/decorators.py 14 10 29% 38-49 /usr/local/lib/python3.8/dist-packages/IPython/utils/dir2.py 34 28 18% 16-20, 38-51, 64-84 /usr/local/lib/python3.8/dist-packages/IPython/utils/encoding.py 23 7 70% 30, 53-58, 64-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/frame.py 17 11 35% 50-53, 66-67, 81-82, 91-94 /usr/local/lib/python3.8/dist-packages/IPython/utils/generics.py 9 2 78% 12, 30 /usr/local/lib/python3.8/dist-packages/IPython/utils/importstring.py 12 10 17% 27-39 /usr/local/lib/python3.8/dist-packages/IPython/utils/io.py 130 76 42% 28-31, 41-42, 47-49, 52-64, 68-73, 84, 122-132, 136-139, 143-145, 149-150, 153-154, 170-188, 207-211, 216-218, 223-227, 232-236, 246-248 /usr/local/lib/python3.8/dist-packages/IPython/utils/ipstruct.py 76 56 26% 85-88, 111-123, 146-151, 165-166, 180-182, 196-198, 212-215, 223-229, 232, 245, 263, 271, 360-390 /usr/local/lib/python3.8/dist-packages/IPython/utils/module_paths.py 11 8 27% 61-70 /usr/local/lib/python3.8/dist-packages/IPython/utils/openpy.py 45 35 22% 24-40, 46-58, 75-79, 100-103 /usr/local/lib/python3.8/dist-packages/IPython/utils/path.py 191 148 23% 27, 30-53, 57, 67, 76-81, 87-90, 99-109, 146-162, 190-211, 220-230, 239-249, 254-256, 260-262, 266-268, 272-274, 278-280, 297-302, 307-311, 321-327, 341-351, 362-363, 375-382, 395-419, 429-436 /usr/local/lib/python3.8/dist-packages/IPython/utils/process.py 29 17 41% 15, 17, 47-50, 55-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/py3compat.py 108 72 33% 18-19, 22-23, 27-29, 32-34, 38-40, 45-59, 66-76, 93-140, 147, 156-158, 165-168, 179, 189 /usr/local/lib/python3.8/dist-packages/IPython/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/IPython/utils/strdispatch.py 33 23 30% 25-26, 31-33, 38-40, 44-52, 55, 58-61, 65-68 /usr/local/lib/python3.8/dist-packages/IPython/utils/sysinfo.py 40 24 40% 54-65, 81-82, 97-99, 119, 123, 128-129, 134, 152-165 /usr/local/lib/python3.8/dist-packages/IPython/utils/syspathcontext.py 32 22 31% 24, 27-31, 34-40, 46, 49-53, 56-62 /usr/local/lib/python3.8/dist-packages/IPython/utils/tempdir.py 23 12 48% 24-26, 29-30, 35, 38, 51-53, 56-57 /usr/local/lib/python3.8/dist-packages/IPython/utils/terminal.py 59 37 37% 27-33, 53, 58, 62, 68-69, 73, 81-105, 110-112, 117-119, 123-125, 129 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/usr/local/lib/python3.8/dist-packages/MySQLdb/constants/CLIENT.py 18 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FIELD_TYPE.py 29 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/FLAG.py 16 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/constants/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/converters.py 35 8 77% 47-48, 52, 56, 65, 79, 82, 85 /usr/local/lib/python3.8/dist-packages/MySQLdb/cursors.py 261 88 66% 83-90, 93, 96-97, 107, 114-120, 127, 142-144, 160, 171, 187, 195-200, 205-206, 227, 259-260, 296-310, 328, 358-363, 368-372, 378, 391-400, 403-405, 417, 421-426, 431-434, 438-441, 444, 447-450 /usr/local/lib/python3.8/dist-packages/MySQLdb/release.py 3 0 100% /usr/local/lib/python3.8/dist-packages/MySQLdb/times.py 76 47 38% 21, 25, 29, 34-37, 43-47, 53, 60-64, 75-76, 79-99, 102-113, 122-123, 127, 131 /usr/local/lib/python3.8/dist-packages/absl/__init__.py 0 0 100% 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169-181, 199-206, 289-292, 359, 378-380, 402-405, 436, 460-462, 487-489, 514-516, 542-544, 577, 598-622 /usr/local/lib/python3.8/dist-packages/absl/flags/_exceptions.py 31 13 58% 64-75, 91-99 /usr/local/lib/python3.8/dist-packages/absl/flags/_flag.py 201 104 48% 88, 106, 121, 124, 127-129, 132, 135, 139-141, 154, 162-167, 182-183, 187-189, 193, 197-208, 228, 247-280, 284, 299, 333-337, 345-349, 352-356, 377-378, 388-393, 396-406, 410-420, 424, 427-432, 444-448, 454-458, 462-466 /usr/local/lib/python3.8/dist-packages/absl/flags/_flagvalues.py 525 404 23% 135-136, 139, 169, 201-206, 217-227, 238-247, 260-263, 276-288, 303-314, 329-340, 349, 365-375, 383-393, 403-404, 410, 414, 416, 418, 420, 423-430, 438, 440, 446, 449, 459, 467, 471-491, 495-503, 506-510, 525-531, 554-561, 578-583, 587, 590, 593, 613-637, 640, 643, 647-649, 661, 684-796, 800, 809, 813-819, 823, 827, 840-857, 870-879, 883-886, 890-892, 903-905, 916-918, 926, 929-963, 976-980, 984-995, 1013-1018, 1041-1086, 1126-1168, 1183-1190, 1203-1204, 1219-1255, 1259, 1264 /usr/local/lib/python3.8/dist-packages/absl/flags/_helpers.py 164 117 29% 29-30, 34-35, 127, 132, 157-158, 174-186, 191-204, 210-233, 238-261, 284-318, 339-356, 371-398, 407-424, 428-431 /usr/local/lib/python3.8/dist-packages/absl/flags/_validators.py 94 61 35% 64-68, 80-82, 90, 93, 104, 128-129, 132, 135, 145, 171-172, 183, 186-190, 193, 222-223, 252-257, 286-288, 320-327, 355-360, 383-384, 405-420, 435-449, 462-463 /usr/local/lib/python3.8/dist-packages/absl/logging/__init__.py 406 221 46% 96, 169, 181-194, 208, 215-216, 220, 223, 266, 280-284, 298-303, 312, 317, 322, 326, 331-333, 338, 343, 348, 382-383, 408-414, 429-430, 473, 476, 499, 515-524, 529, 534, 539, 544, 552, 568-575, 596-620, 633-647, 656-668, 704-718, 722, 727, 744-772, 776-779, 783-790, 803-808, 827-850, 854-870, 883, 886, 889, 892, 895-896, 899-902, 906, 913, 916, 931-938, 989, 993-996, 998, 1002, 1006, 1010, 1014-1017, 1021, 1025, 1029, 1046-1047, 1064-1065, 1083-1086, 1099-1100, 1110, 1119-1121, 1133-1148, 1156 /usr/local/lib/python3.8/dist-packages/absl/logging/converter.py 62 32 48% 107-114, 129-135, 151, 153, 157, 169, 184-200, 215 /usr/local/lib/python3.8/dist-packages/absl/testing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/testing/_pretty_print_reporter.py 50 28 44% 31-32, 35-40, 43-44, 47-48, 51-52, 55-56, 59-60, 63-64, 67-68, 84-87, 91-93, 96 /usr/local/lib/python3.8/dist-packages/absl/testing/absltest.py 885 696 21% 52-54, 75-76, 82-88, 94-97, 131-135, 210-223, 270, 276-278, 300-303, 315, 321, 348-350, 363-371, 383, 391-417, 423, 428-429, 434-435, 449-452, 463-464, 482-488, 504-510, 518-520, 538-542, 545-549, 575-590, 634-639, 661-665, 670, 674, 678-680, 684-692, 696-700, 704-707, 722-732, 742-743, 753-754, 764-765, 775-776, 792-817, 827-834, 843-850, 860-865, 890-911, 915-919, 924-928, 942-946, 951-978, 1004-1032, 1038-1065, 1069-1073, 1078-1085, 1119-1146, 1164-1182, 1210-1224, 1240-1243, 1246, 1249-1255, 1275-1283, 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/usr/local/lib/python3.8/dist-packages/absl/third_party/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/case.py 187 177 5% 15-63, 70-235, 239-273 /usr/local/lib/python3.8/dist-packages/absl/third_party/unittest3_backport/result.py 16 9 44% 16-25 /usr/local/lib/python3.8/dist-packages/appdirs.py 257 211 18% 29-39, 77-97, 131-163, 195-203, 236-254, 291-300, 302-304, 310, 345-353, 388-404, 411-415, 419, 424, 429, 434, 439, 444, 449, 460-476, 480-503, 507-530, 533-556, 559-571, 577-608 /usr/local/lib/python3.8/dist-packages/astunparse/__init__.py 13 3 77% 18-20 /usr/local/lib/python3.8/dist-packages/astunparse/printer.py 37 28 24% 10-12, 16, 19, 23-51 /usr/local/lib/python3.8/dist-packages/astunparse/unparser.py 718 440 39% 77-78, 81-82, 85, 93-97, 100-101, 123-126, 129-139, 151, 154, 157-158, 161-165, 168-175, 178-189, 192-193, 196-197, 200-205, 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0 0 100% /usr/local/lib/python3.8/dist-packages/boto/compat.py 47 26 45% 28-29, 35-36, 45-47, 68-102 /usr/local/lib/python3.8/dist-packages/boto/connection.py 605 493 19% 79-80, 84-85, 123, 131, 138, 146-158, 173-181, 189-190, 197-199, 234-238, 243-246, 249, 255, 264-269, 276-280, 290-300, 341-358, 361, 367-384, 390-391, 402-413, 474-572, 575, 578, 581, 587, 591, 594, 598, 602, 608, 614, 622-642, 645-662, 665-698, 701-705, 708-721, 724-778, 781, 784-851, 854-855, 858-859, 863-874, 877-881, 884, 897-1033, 1037-1059, 1066-1070, 1077-1078, 1091, 1103, 1106, 1109-1116, 1119-1122, 1158-1162, 1168-1186, 1190-1208, 1211-1227 /usr/local/lib/python3.8/dist-packages/boto/endpoints.py 79 57 28% 44-50, 55-56, 61-78, 81-82, 87-92, 100-103, 118-121, 126, 130, 149, 163-166, 177, 186, 197, 210-222, 227-232, 237-239 /usr/local/lib/python3.8/dist-packages/boto/exception.py 287 166 42% 42-43, 46, 49, 79-135, 138-142, 145-148, 151, 155, 159, 162-170, 173-176, 181-185, 188, 191-196, 204-205, 208-211, 254-256, 259, 262-267, 270-272, 280-281, 284, 287, 295-296, 299, 303-306, 310-312, 334-340, 343-347, 350-353, 356-359, 376-383, 403-405, 408, 411-416, 458-459, 466-467, 474-475, 482-483, 490-491, 532-534, 537, 549-551, 554, 565-566, 574-575, 578, 592-593 /usr/local/lib/python3.8/dist-packages/boto/gs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/gs/acl.py 187 133 29% 58-59, 63, 67-75, 80-82, 87-88, 91-93, 96-97, 100-107, 110-115, 118-126, 132-135, 138-141, 144-149, 152-155, 158-164, 172-175, 178, 181-205, 208-216, 219-223, 243-250, 254-264, 267-271, 274-284, 287-308 /usr/local/lib/python3.8/dist-packages/boto/gs/user.py 26 20 23% 25-29, 32, 35, 38-43, 46-54 /usr/local/lib/python3.8/dist-packages/boto/handler.py 29 19 34% 30-32, 35-38, 41-46, 49, 54-57, 60 /usr/local/lib/python3.8/dist-packages/boto/https_connection.py 49 34 31% 41-44, 47, 59-62, 75-83, 105-114, 118-135 /usr/local/lib/python3.8/dist-packages/boto/jsonresponse.py 108 89 18% 30-32, 35-41, 44-47, 50, 53-55, 64-74, 77-86, 89-91, 94-109, 112-119, 127-132, 135-137, 140-155, 158-168 /usr/local/lib/python3.8/dist-packages/boto/plugin.py 38 22 42% 53-56, 60-66, 70-78, 86, 91-93 /usr/local/lib/python3.8/dist-packages/boto/provider.py 247 178 28% 183-214, 217-219, 222, 227-229, 232, 237-239, 242, 247-263, 267-378, 383-402, 413-434, 437-441, 444-465, 468-473, 476, 479, 484 /usr/local/lib/python3.8/dist-packages/boto/pyami/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/pyami/config.py 158 108 32% 42, 47-49, 59, 61, 65-69, 78, 88-93, 96-103, 111-121, 124, 127, 130-134, 137-141, 144-148, 151, 166-169, 172-180, 183-186, 189-191, 194-202, 205-217, 220-235 /usr/local/lib/python3.8/dist-packages/boto/regioninfo.py 89 66 26% 44, 56-57, 77-82, 100-115, 121-134, 161-182, 208-220, 226-229, 235-239, 247-249, 259-262, 265, 268, 271-276, 289-290 /usr/local/lib/python3.8/dist-packages/boto/resultset.py 111 98 12% 47-62, 65-76, 79-82, 85-134, 140-142, 145-148, 151, 154, 157-160, 163-176 /usr/local/lib/python3.8/dist-packages/boto/s3/__init__.py 17 11 35% 43-44, 54-55, 63-74 /usr/local/lib/python3.8/dist-packages/boto/s3/acl.py 111 89 20% 34-36, 39-51, 54-64, 67-72, 75-82, 88-89, 92, 95-97, 100-101, 104-108, 111-114, 117-121, 130-135, 138-140, 143-156, 159-171 /usr/local/lib/python3.8/dist-packages/boto/s3/bucket.py 700 576 18% 71, 95-97, 100, 103, 106, 109, 112-117, 131, 143, 175-194, 197-231, 282, 328, 363, 369-390, 394-411, 424-426, 469-472, 521-522, 535, 606-609, 623-625, 630, 662-730, 757-759, 766-788, 847-889, 894-908, 912-922, 926-936, 940-944, 948-963, 989-1002, 1028-1040, 1043-1046, 1072-1080, 1110-1119, 1123-1124, 1134-1146, 1162-1171, 1193-1197, 1206-1207, 1216-1227, 1235-1240, 1243-1249, 1253-1260, 1288-1308, 1323-1339, 1350-1366, 1377-1389, 1396-1404, 1438-1441, 1448, 1454-1461, 1483, 1489-1493, 1522-1526, 1530-1538, 1544-1551, 1560-1563, 1570-1576, 1586-1594, 1598-1606, 1618-1632, 1644, 1651-1658, 1669-1673, 1679-1687, 1736-1767, 1775-1806, 1815-1822, 1826, 1829-1835, 1838-1845, 1849-1864, 1867, 1870-1878 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlistresultset.py 67 54 19% 29-41, 54-59, 62, 73-86, 99-105, 108, 121-134, 147-151, 154 /usr/local/lib/python3.8/dist-packages/boto/s3/bucketlogging.py 50 41 18% 28-33, 36-47, 50, 53-57, 60-65, 69-83 /usr/local/lib/python3.8/dist-packages/boto/s3/connection.py 282 212 25% 58-62, 67-69, 76, 79-82, 85-88, 91-95, 98-99, 106, 113, 119, 122-126, 132-135, 176-199, 205-208, 211-212, 215, 226, 232-237, 298-355, 361-380, 386-438, 443-453, 468-469, 508-511, 528-555, 577-581, 606-627, 644-647, 653-667 /usr/local/lib/python3.8/dist-packages/boto/s3/cors.py 80 69 14% 65-78, 81, 84, 87-100, 103-117, 126-130, 133, 140-144, 194-210 /usr/local/lib/python3.8/dist-packages/boto/s3/deletemarker.py 28 23 18% 26-31, 34-38, 41-55 /usr/local/lib/python3.8/dist-packages/boto/s3/key.py 690 590 14% 106-135, 138-147, 150, 154-157, 160, 163, 168-169, 172-175, 180-184, 187-192, 197-207, 210, 219-223, 226-231, 234-242, 246-259, 262-273, 280, 304-334, 348, 352-361, 381-385, 397-402, 408-415, 441-451, 501-507, 515-519, 522-542, 551, 557, 561, 566-573, 576, 580-581, 584-585, 588-589, 592-593, 596, 605-610, 624-635, 639, 695-713, 760, 767-966, 969-1021, 1036-1045, 1108-1132, 1214-1311, 1374-1375, 1437-1444, 1493, 1503-1575, 1602, 1658-1664, 1722-1739, 1795-1804, 1829-1831, 1853-1856, 1859-1867, 1875-1889, 1893-1912, 1929-1935 /usr/local/lib/python3.8/dist-packages/boto/s3/keyfile.py 74 53 28% 35-44, 47-49, 52-85, 88-89, 92-94, 97, 102, 107, 110, 113, 116, 119, 122, 125, 128, 131, 134 /usr/local/lib/python3.8/dist-packages/boto/s3/lifecycle.py 148 113 24% 48-65, 68, 71-76, 79-86, 89-99, 111-112, 115, 118-121, 124-128, 131-137, 152-154, 157-161, 164-171, 178-182, 185, 188-203, 210-213, 230-231, 234-236, 242, 246, 250, 259-263, 266, 273-278, 310-311 /usr/local/lib/python3.8/dist-packages/boto/s3/multidelete.py 64 48 25% 41-44, 47-50, 53, 56-66, 82-85, 88-92, 95, 98-107, 121-123, 126-134, 137 /usr/local/lib/python3.8/dist-packages/boto/s3/multipart.py 160 133 17% 46-52, 55, 59, 62-71, 86-90, 93-96, 99, 102-111, 118-125, 134-146, 149, 152, 155-162, 165-175, 178-200, 211-226, 253-261, 290-301, 317-318, 330 /usr/local/lib/python3.8/dist-packages/boto/s3/prefix.py 16 10 38% 24-25, 28, 31-34, 38-41 /usr/local/lib/python3.8/dist-packages/boto/s3/tagging.py 52 34 35% 7-8, 11, 14-17, 20, 24, 29-33, 36, 39-40, 43-47, 54-58, 61, 64-68, 71 /usr/local/lib/python3.8/dist-packages/boto/s3/user.py 23 18 22% 24-28, 31, 34-39, 42-49 /usr/local/lib/python3.8/dist-packages/boto/s3/website.py 122 85 30% 24-26, 57-63, 66-72, 75, 78-89, 94-98, 101, 104-106, 109-114, 131-133, 136, 152-153, 156-159, 162, 165, 168-171, 191-192, 195-198, 201, 204-209, 213, 218-223, 245-247, 250, 283-288, 291 /usr/local/lib/python3.8/dist-packages/boto/storage_uri.py 488 377 23% 57, 62, 66, 69-70, 77-78, 82-83, 87-89, 103-149, 152, 158-160, 165-172, 176-177, 180-184, 187-191, 194-196, 199-203, 209-218, 224-227, 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744-750, 753-765, 780-782, 785-787, 790, 793-797, 800-803, 808-859, 863-872, 876-881, 897-899, 920-944, 959-972, 1000, 1004-1029, 1038, 1048-1049, 1060, 1070-1083, 1093-1098 /usr/local/lib/python3.8/dist-packages/boto/vendored/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/exceptions.py 3 0 100% /usr/local/lib/python3.8/dist-packages/boto/vendored/regions/regions.py 81 60 26% 58, 65, 85, 94-96, 99-102, 106-116, 120-124, 128-152, 156-160, 163-177, 180-182, 186 /usr/local/lib/python3.8/dist-packages/boto/vendored/six.py 444 208 53% 49-72, 98-99, 112, 120-121, 131-133, 145, 154-157, 192-193, 222-223, 304, 480, 488, 493-499, 511-517, 522-524, 530-532, 537, 542, 546-560, 575, 578, 581, 584, 592-608, 620, 623, 636-637, 642-661, 667, 671, 675, 682-701, 707, 717-718, 723-775, 777-784, 789-795, 805-809, 814-825, 836-843, 864-865 /usr/local/lib/python3.8/dist-packages/cachetools/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/cachetools/abc.py 25 14 44% 21-24, 29-36, 39-43 /usr/local/lib/python3.8/dist-packages/cachetools/cache.py 60 38 37% 6, 9, 12, 21-27, 30, 38-41, 44-57, 60-62, 65, 68, 71, 74, 79, 84, 89 /usr/local/lib/python3.8/dist-packages/cachetools/decorators.py 73 69 5% 11-44, 52-88 /usr/local/lib/python3.8/dist-packages/cachetools/keys.py 24 14 42% 17-20, 23, 26, 29, 40-43, 49-52 /usr/local/lib/python3.8/dist-packages/cachetools/lfu.py 22 14 36% 10-11, 14-16, 19-20, 23-24, 28-33 /usr/local/lib/python3.8/dist-packages/cachetools/lru.py 27 18 33% 10-11, 14-16, 19-20, 23-24, 28-33, 36-39 /usr/local/lib/python3.8/dist-packages/cachetools/rr.py 19 11 42% 8, 15-20, 25, 29-34 /usr/local/lib/python3.8/dist-packages/cachetools/ttl.py 156 118 24% 12-13, 16, 19-22, 28-29, 32-35, 38-43, 46, 49, 52, 59-64, 67-72, 75-84, 87-99, 102-106, 109-116, 119-126, 129-136, 139-141, 145-147, 152, 157, 161-172, 175-177, 180-181, 184-185, 188-189, 196-203, 206-208 /usr/local/lib/python3.8/dist-packages/cycler.py 177 107 40% 73, 110, 118, 122, 127, 131, 157-178, 185-189, 218-223, 229, 240-243, 255-262, 265, 268-273, 284-292, 303-311, 317-322, 325-333, 337-347, 396-397, 425, 454-465, 509, 513-516, 519-526, 548-556 /usr/local/lib/python3.8/dist-packages/cython.py 10 6 40% 9-17 /usr/local/lib/python3.8/dist-packages/decorator.py 270 120 56% 49-60, 64-67, 70-73, 100, 114-115, 117, 119-120, 133, 139, 143, 155-156, 168, 174, 186-189, 217, 233-235, 241, 246, 271, 279, 282-283, 291, 299-300, 306, 314-316, 318, 321, 329, 339-348, 357-454 /usr/local/lib/python3.8/dist-packages/defusedxml/ElementTree.py 64 25 61% 21-23, 52, 79-105, 108, 113, 120 /usr/local/lib/python3.8/dist-packages/defusedxml/__init__.py 21 15 29% 25-51 /usr/local/lib/python3.8/dist-packages/defusedxml/common.py 65 42 35% 23, 31-34, 37-38, 46-52, 55-56, 64-68, 71-72, 81-90, 98-105, 115-122, 125-132 /usr/local/lib/python3.8/dist-packages/gast/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/gast/ast3.py 192 145 24% 10-160, 174, 189-194, 200-207, 214-232, 239, 260-264, 270-306, 310-376, 391 /usr/local/lib/python3.8/dist-packages/gast/astn.py 24 1 96% 21 /usr/local/lib/python3.8/dist-packages/gast/gast.py 78 46 41% 9-11, 292, 302-304, 308-316, 327-331, 342-366, 375-380 /usr/local/lib/python3.8/dist-packages/h5py/__init__.py 57 23 60% 27-32, 37, 89-90, 97-113 /usr/local/lib/python3.8/dist-packages/h5py/_hl/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/h5py/_hl/attrs.py 133 92 31% 63, 76-78, 90, 100, 105, 122-221, 232-247, 253, 257-275, 284-286 /usr/local/lib/python3.8/dist-packages/h5py/_hl/base.py 216 108 50% 23-24, 40-45, 53-63, 88, 112, 126-128, 131, 134, 139-141, 144, 155-162, 185, 188-193, 197-199, 204-207, 219-221, 227, 237-239, 251, 265, 281, 285-287, 291, 294-295, 308, 312, 326, 340-344, 347-349, 359-363, 366-368, 381-404, 409, 413, 417, 421, 447, 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113-244, 263-283, 302-342 /usr/local/lib/python3.8/dist-packages/h5py/_hl/group.py 234 168 28% 41, 62-69, 132-140, 158-174, 189-206, 223-238, 247-253, 260-262, 271-274, 301-342, 368-393, 399, 404, 409-410, 415, 455-494, 504-507, 530-534, 560-565, 569-579, 603, 606, 609, 622, 627, 630-631, 634 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections.py 295 221 25% 58-95, 111, 117-118, 150-151, 171, 176, 180-182, 185, 197-207, 211-218, 222, 226, 230, 252-269, 279-283, 293, 299, 304, 315-320, 337, 340-341, 345-414, 417-419, 424-442, 453-475, 482-488, 495-508, 519-598 /usr/local/lib/python3.8/dist-packages/h5py/_hl/selections2.py 43 33 23% 26-45, 57-74, 83-91, 94-95, 102-105 /usr/local/lib/python3.8/dist-packages/h5py/_hl/vds.py 52 33 37% 56-90, 94, 97-99, 120-123, 126-127 /usr/local/lib/python3.8/dist-packages/h5py/h5py_warnings.py 19 6 68% 16-17, 34-36, 39 /usr/local/lib/python3.8/dist-packages/h5py/version.py 21 3 86% 32, 34, 36 /usr/local/lib/python3.8/dist-packages/imagecodecs/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/imagecodecs/imagecodecs.py 300 226 25% 330, 350-351, 356, 371, 375-383, 387, 390, 395, 401-402, 408-479, 484, 489-510, 515-603, 608-641, 646-666, 675, 684, 689, 698, 703-706, 711-722, 727-734, 745-788, 796-802, 814 /usr/local/lib/python3.8/dist-packages/imageio/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/imageio/core/fetching.py 105 88 16% 62-114, 150-184, 210-225, 230-232, 237-247 /usr/local/lib/python3.8/dist-packages/imageio/core/findlib.py 74 65 12% 23-29, 37-81, 99-160 /usr/local/lib/python3.8/dist-packages/imageio/core/format.py 276 178 36% 94, 103, 108, 111, 115, 118, 126, 142, 149, 155, 164-170, 179-185, 192, 199, 216-221, 228, 235, 238-239, 242-244, 247-248, 256-261, 267, 272-275, 289, 300, 331, 343-349, 359, 367-370, 386-392, 401-414, 419, 422-425, 437, 447, 458, 484-502, 517-521, 527, 531, 551, 557, 560-565, 569-609, 633, 635, 655, 657, 659-665, 678-697, 705-725, 735 /usr/local/lib/python3.8/dist-packages/imageio/core/functions.py 157 124 21% 100-121, 139-142, 172-186, 213-231, 259-267, 291-308, 354-374, 397-427, 453-455, 479-496, 542-561, 586-615 /usr/local/lib/python3.8/dist-packages/imageio/core/request.py 319 274 14% 19-20, 89-128, 133-262, 271, 279, 289, 295, 310-350, 359-372, 381-421, 427-428, 435-437, 440-467, 476-482, 492-496, 501-530, 533, 539-561, 564-565, 568, 571 /usr/local/lib/python3.8/dist-packages/imageio/core/util.py 263 210 20% 30-34, 38-42, 55-108, 122-134, 139-143, 149, 155-158, 164-169, 180-185, 205-211, 214-225, 228-230, 252-257, 265-273, 281, 290-309, 316, 324-328, 335-339, 346-350, 355, 358, 361, 364, 376-383, 387-395, 398-400, 404-408, 423-466, 479-491, 506-520, 531-542, 548-555 /usr/local/lib/python3.8/dist-packages/imageio/plugins/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/imageio/plugins/_freeimage.py 603 405 33% 63-67, 74-87, 422, 429-432, 439-447, 450-454, 461-480, 485-514, 518-521, 526-527, 530-531, 537, 544-549, 555-557, 564, 573-603, 609, 615, 620-625, 628, 631-640, 645-652, 660-723, 728-797, 807-839, 842-856, 876-895, 941-975, 980-1031, 1041-1083, 1089-1110, 1113-1163, 1170-1187, 1220-1245, 1271-1295, 1298-1299, 1305-1321, 1326-1328 /usr/local/lib/python3.8/dist-packages/imageio/plugins/bsdf.py 140 113 19% 14-52, 62-63, 66-73, 76, 126-130, 133-135, 143-180, 185, 188-194, 198-231, 240-249, 256-257, 261-289 /usr/local/lib/python3.8/dist-packages/imageio/plugins/dicom.py 147 122 17% 31-33, 41-56, 89-100, 105, 111-155, 159-161, 165-168, 171-199, 202-231, 239-266 /usr/local/lib/python3.8/dist-packages/imageio/plugins/example.py 43 23 47% 54-56, 67-69, 82-84, 89, 93, 97-106, 111, 124, 129, 133, 138 /usr/local/lib/python3.8/dist-packages/imageio/plugins/feisem.py 41 32 22% 27, 38-41, 55-84 /usr/local/lib/python3.8/dist-packages/imageio/plugins/ffmpeg.py 304 258 15% 27, 30-31, 57-66, 178-189, 192-194, 204-239, 257-327, 339-344, 357-358, 361, 370-386, 389, 394-468, 472-474, 478-502, 523-527, 530-532, 537-572, 575, 582-617, 629-636, 639-641, 646-649, 653-661, 667-698 /usr/local/lib/python3.8/dist-packages/imageio/plugins/fits.py 40 24 40% 15-23, 80, 84, 90-102, 105, 108, 112-116, 120 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimage.py 177 122 31% 48, 52-59, 63-70, 76, 79-80, 83, 86-88, 91-93, 99-102, 106-109, 113-132, 135, 168-174, 177-178, 215-219, 225-240, 243-259, 303-309, 312-314, 323-340, 349-362, 365-368, 393-397, 485-510 /usr/local/lib/python3.8/dist-packages/imageio/plugins/freeimagemulti.py 144 104 28% 27-32, 35, 38, 41-45, 48-55, 62-67, 71, 75-86, 90-94, 97, 137-140, 197-200, 203-205, 225-254, 263-299, 308-322 /usr/local/lib/python3.8/dist-packages/imageio/plugins/gdal.py 35 19 46% 15-23, 40-43, 46, 52-54, 57, 60, 63-65, 68 /usr/local/lib/python3.8/dist-packages/imageio/plugins/grab.py 63 37 41% 25, 28-40, 44, 47, 50, 66-70, 73-79, 95-99, 102-111 /usr/local/lib/python3.8/dist-packages/imageio/plugins/lytro.py 304 225 26% 63, 69, 74, 78, 83, 101-103, 109-136, 142-143, 148, 152, 157-171, 177-195, 212-214, 220-280, 285, 289, 295-302, 308-312, 319-325, 348-371, 375-383, 388-391, 410-412, 418-435, 441-442, 447, 451, 456-470, 476-494, 511-513, 519-559, 564, 568, 574-582, 588-592, 599-607, 630-653, 657-665, 670-673 /usr/local/lib/python3.8/dist-packages/imageio/plugins/npz.py 37 17 54% 40, 44, 51-55, 58, 61, 65-69, 73, 81, 85, 88, 91 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow.py 365 285 22% 76, 79-99, 102-108, 111-115, 119-146, 149-150, 153-155, 159, 162-165, 168-181, 184-186, 190-197, 200, 203-218, 221, 298, 301-315, 323-357, 360-364, 429, 433-440, 443-455, 461-478, 486-496, 499-503, 564, 568-575, 578-590, 596-613, 621-639, 642-648, 653-655, 667-680, 684-695, 705-792, 797-835 /usr/local/lib/python3.8/dist-packages/imageio/plugins/pillow_info.py 5 1 80% 100 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50 /usr/local/lib/python3.8/dist-packages/ipywidgets/_version.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/docutils.py 6 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/domwidget.py 27 14 48% 26-28, 36-38, 41-50 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/interaction.py 289 226 22% 11-12, 17-18, 33-34, 51-62, 73-85, 90-93, 99-128, 132-152, 177-232, 245-268, 272, 278-290, 295-307, 312-343, 348-359, 364-380, 388-393, 444, 507-538, 555-556, 570, 576 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/trait_types.py 78 33 58% 45-52, 84-87, 100-103, 125-128, 137-140, 162-165, 168, 195-206, 220 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/util.py 8 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/valuewidget.py 12 5 58% 20, 24-27 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget.py 403 283 30% 13-15, 32-39, 42-49, 59, 65-71, 80-116, 129-131, 141-162, 173-174, 183-195, 208-211, 216-223, 243-256, 259-265, 281-283, 302-303, 312, 317-318, 323-339, 349-354, 357-369, 372, 398, 411-415, 419, 427-438, 443-448, 455, 467-471, 481-489, 506-523, 526, 529-533, 540-545, 557, 571, 586, 590-594, 600-606, 609, 624-628, 633-642, 646-662, 668-689, 693, 697, 702, 707, 712-732, 736-737, 740-755, 758-763 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_bool.py 31 3 90% 22-24 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_box.py 37 5 86% 63-65, 68-69 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_button.py 41 12 71% 61-63, 68-73, 86, 94, 104-105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_color.py 14 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_controller.py 29 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_core.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_date.py 13 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_description.py 23 6 74% 28-34 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_float.py 168 62 63% 24-26, 36-39, 44-49, 54-59, 70-73, 78-83, 88-93, 262, 266, 270, 274, 278-281, 290-294, 298-308, 312-315 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_int.py 185 67 64% 42-44, 53-61, 73-75, 85-93, 98-101, 106-111, 116-121, 202, 206, 210, 214, 218-221, 243-247, 251-261, 265-268 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_layout.py 61 7 89% 82-85, 93-96 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_link.py 36 15 58% 24-34, 50-52, 56, 75, 105 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_media.py 102 46 55% 44-51, 71-75, 86-88, 92-96, 101-111, 116-133, 159, 163, 166, 194, 197, 223, 226 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_output.py 64 33 48% 76-77, 97-105, 109-113, 117-127, 131-132, 136, 142, 146, 157-159 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selection.py 295 148 50% 11-12, 119-132, 136-139, 175-191, 196-200, 205-218, 222-225, 230-235, 239-243, 247-254, 258-260, 264-271, 274-280, 316-327, 331-335, 340-344, 349-352, 357-363, 368-371, 375-377, 381-383, 387-389, 392-395, 529-531, 535-540, 544-547, 553-555, 559-565, 615-619, 624-629 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_selectioncontainer.py 36 14 61% 27-30, 44-46, 57-61, 65-68 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_string.py 68 15 78% 30-32, 79-81, 91-92, 106-108, 120-123 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_style.py 9 0 100% /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_templates.py 198 138 30% 80-85, 89-91, 95-96, 100-106, 157-158, 162-167, 172, 176-242, 247, 281-289, 293-295, 300-312, 315-330, 333-345, 349-355, 397-398, 403-450, 454 /usr/local/lib/python3.8/dist-packages/ipywidgets/widgets/widget_upload.py 39 11 72% 24-26, 58-64, 68 /usr/local/lib/python3.8/dist-packages/jedi/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jedi/_compatibility.py 339 252 26% 18-19, 34-45, 52-53, 56, 59, 63-88, 92-111, 115-150, 158-162, 169-205, 223-224, 230-233, 246-248, 266, 273-275, 285, 311-312, 319, 324-325, 329-330, 334-335, 339-340, 350-352, 360-370, 376, 387-396, 404-410, 414-426, 432-440, 445-451, 456-466, 483-530, 536-582, 591-624 /usr/local/lib/python3.8/dist-packages/jedi/api/__init__.py 341 241 29% 58-62, 127-203, 209-248, 251, 254, 275, 278-283, 286-291, 310-311, 314-319, 322-342, 345-350, 370-371, 375-408, 422, 425, 429-431, 454, 471-480, 483-488, 502-514, 517-522, 541-558, 569-600, 603-634, 648-649, 657, 661-671, 683, 686-687, 715, 719-727, 765, 769-777, 800-801, 830-845, 849-855, 863-869, 884-886, 898-901 /usr/local/lib/python3.8/dist-packages/jedi/api/classes.py 297 193 35% 37, 47-49, 53, 81-86, 93, 98-104, 115, 175-187, 202, 208-211, 216-219, 224-227, 260-270, 273, 276, 311-329, 355-368, 374-377, 384-387, 402-403, 406-411, 416-427, 447-448, 451-466, 472-489, 497-522, 525, 542-552, 555-561, 570, 582, 594, 604-613, 616-624, 647-649, 664, 670-675, 678-684, 687-693, 696-697, 709-716, 719, 728, 732-738, 747-748, 758-761, 764, 770, 773, 782-783, 793, 803, 812-814, 824, 836, 839, 853, 861, 870, 879-883 /usr/local/lib/python3.8/dist-packages/jedi/api/completion.py 348 296 15% 30, 35-40, 44-68, 72-73, 80-81, 85-89, 96, 102-114, 117-151, 174-282, 285-295, 298-317, 320-323, 326-337, 340-355, 358-360, 363-365, 371-389, 401-421, 424-431, 442-448, 455-499, 503-516, 539-577, 583-619 /usr/local/lib/python3.8/dist-packages/jedi/api/completion_cache.py 19 11 42% 5-9, 14-19 /usr/local/lib/python3.8/dist-packages/jedi/api/environment.py 219 166 24% 36-37, 41-45, 49, 65-67, 70-107, 110-111, 114, 129, 134-136, 145, 148, 157-169, 173-177, 190-198, 202-238, 242-252, 257-264, 284-321, 334-338, 351-363, 374-377, 385-393, 398-420, 424-425, 432-454, 458-473, 477-480 /usr/local/lib/python3.8/dist-packages/jedi/api/errors.py 19 7 63% 8, 16, 21, 26, 31, 36, 39 /usr/local/lib/python3.8/dist-packages/jedi/api/exceptions.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/api/file_name.py 115 103 10% 17-54, 64-81, 85-99, 103-156 /usr/local/lib/python3.8/dist-packages/jedi/api/helpers.py 319 272 15% 26, 30-35, 39-42, 47, 51-61, 66-71, 77, 83-117, 124-156, 163-179, 183-201, 206-209, 213, 217, 220-268, 272-336, 343-357, 361-371, 375-421, 427-439, 449-465, 474-493, 497-500 /usr/local/lib/python3.8/dist-packages/jedi/api/interpreter.py 23 12 48% 12, 19, 24-25, 28, 34-41 /usr/local/lib/python3.8/dist-packages/jedi/api/keywords.py 34 25 26% 8-15, 22, 30-57 /usr/local/lib/python3.8/dist-packages/jedi/api/project.py 214 164 23% 40-51, 56-61, 65, 78, 82, 92-98, 106-118, 139-151, 156-161, 169-205, 208-213, 236, 248, 251, 256-343, 346, 350-353, 358-362, 375-411, 415 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/__init__.py 133 110 17% 19-23, 26-36, 39, 42-48, 51, 56-58, 64-73, 89, 92-98, 104-108, 112-118, 122-138, 142-218, 225 /usr/local/lib/python3.8/dist-packages/jedi/api/refactoring/extract.py 239 210 12% 20-29, 36-41, 49-93, 100-126, 130, 134-138, 147-149, 153, 160-164, 172-200, 204, 209-292, 296-306, 310-316, 320-337, 341-353, 357-363, 371-379, 383-386 /usr/local/lib/python3.8/dist-packages/jedi/api/strings.py 64 47 27% 27-50, 54-58, 68-77, 81-86, 90-93, 97-98, 102-109 /usr/local/lib/python3.8/dist-packages/jedi/cache.py 65 40 38% 32-43, 60-73, 84-93, 105-113 /usr/local/lib/python3.8/dist-packages/jedi/common/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jedi/common/utils.py 24 18 25% 6-13, 21-26, 31-36 /usr/local/lib/python3.8/dist-packages/jedi/common/value.py 54 31 43% 3-4, 7-11, 48, 55, 59-61, 68-74, 77, 80, 83-84, 87, 90, 93, 96, 99-104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/jedi/debug.py 80 50 38% 22, 36-56, 74-75, 81-82, 89-97, 103-109, 113-120, 124-127, 136-140 /usr/local/lib/python3.8/dist-packages/jedi/file_io.py 54 30 44% 8, 11, 14, 17, 20, 23, 28, 31, 34, 37, 40-57, 62, 68-69, 72-75 /usr/local/lib/python3.8/dist-packages/jedi/inference/__init__.py 107 76 29% 86-108, 111, 117-121, 126-130, 135-136, 139-140, 144, 147-179, 183-194, 197 /usr/local/lib/python3.8/dist-packages/jedi/inference/analysis.py 126 101 20% 32-37, 41, 45, 50-51, 54, 58, 61, 65, 68, 71, 81-91, 98-109, 116-127, 138-217 /usr/local/lib/python3.8/dist-packages/jedi/inference/arguments.py 228 156 32% 19-31, 53-68, 76-108, 135, 138, 148-172, 180-183, 188, 191-231, 234-239, 242-247, 250, 253-279, 284, 287-288, 291, 296, 300, 304, 308, 311, 314, 317, 321-333, 337-349 /usr/local/lib/python3.8/dist-packages/jedi/inference/base_value.py 285 179 37% 27-34, 39, 42, 45-47, 50, 53, 56, 62-70, 73-77, 84-98, 101-104, 107-120, 123-126, 130-132, 136, 152, 155-163, 166, 169-176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 210, 213-218, 221-223, 226-227, 230-231, 234-235, 241, 244-245, 249, 253, 256, 260, 263, 266, 274, 283-289, 294, 297-298, 305-306, 309, 312, 317, 320, 325-326, 329, 334-335, 338, 341, 344, 349-371, 376, 379-382, 387, 390, 393, 396, 399, 402-410, 413, 416, 419-436, 440-448, 456 /usr/local/lib/python3.8/dist-packages/jedi/inference/cache.py 72 44 39% 25-45, 80, 90-121 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/__init__.py 38 26 32% 8-16, 25-26, 29-32, 35-37, 40, 48-53, 57, 63-68 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/access.py 327 243 26% 82-98, 109-112, 117, 121-140, 145, 151, 154, 158-159, 167-175, 180-181, 184, 187, 190, 193, 196-199, 202, 205-219, 222, 225-227, 230-234, 237-250, 253, 256, 259-265, 270-285, 288, 291, 294, 297, 300, 303-313, 316, 319-323, 327-351, 354-396, 399-401, 404, 407-409, 412-421, 424-425, 428-456, 459-461, 467-482, 485, 488-490, 493, 507-516, 519-538, 541, 548-552, 557-564 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/getattr_static.py 97 81 16% 16-21, 25-31, 35-39, 43-54, 58-65, 73-127, 131, 135, 152-184 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/mixed.py 155 108 30% 47-49, 52, 58, 63-66, 69-72, 75-78, 81-83, 86, 96, 108-109, 113-117, 121-129, 134-135, 138, 146, 156-175, 179-249, 256-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/__init__.py 236 176 25% 20-21, 37-38, 45-50, 54, 58-70, 75-77, 80-87, 90, 93, 103, 108-110, 113-129, 132-147, 150-151, 160-162, 165-166, 176-206, 211-219, 222, 225-226, 229-273, 283, 288-292, 295-308, 311-327, 330-357, 362-364, 367, 370-374, 377, 380, 383-388, 396-398, 402 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/subprocess/functions.py 66 45 32% 15, 19, 23-24, 28, 35-43, 47, 54, 61-66, 74-78, 82, 86, 91-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/compiled/value.py 394 269 32% 36-41, 46-47, 50-69, 73, 77, 84, 90, 93, 96, 99, 102, 105, 108, 111, 114, 118-133, 136-137, 140, 144-148, 152, 155, 159, 162-170, 173-178, 188-198, 201, 205-208, 211-228, 231-236, 239-245, 248-267, 270, 273, 276, 280, 283, 289-291, 298, 301-304, 307, 312-315, 318, 323-326, 329-330, 333-336, 339-343, 346-350, 354-358, 362, 365, 370-371, 375, 378-383, 386, 389-397, 402-404, 407, 410-413, 416, 421-423, 433-434, 437, 442-444, 447-448, 460-478, 482-485, 488-507, 510, 517, 537-587, 591-600, 606, 611-618, 624-629 /usr/local/lib/python3.8/dist-packages/jedi/inference/context.py 294 199 32% 20-21, 25, 28-34, 41-86, 89-106, 109-112, 115, 118, 121, 124, 127, 130, 133, 137, 140, 144, 147, 150, 154-159, 167-168, 172, 176, 179, 182, 185, 188, 191, 194, 197, 200, 204, 207, 210, 213, 216, 221-222, 225-248, 251-287, 290-297, 302, 312, 315-327, 330, 334, 338, 346, 351, 354, 358, 361, 366, 369, 378-380, 383, 386, 389, 392, 397, 404, 408, 411, 418-432, 483-500 /usr/local/lib/python3.8/dist-packages/jedi/inference/docstrings.py 144 116 19% 52-56, 61-75, 82-100, 108-133, 154-161, 179-183, 187-235, 244-245, 256-268, 273-290, 296-307 /usr/local/lib/python3.8/dist-packages/jedi/inference/filters.py 215 115 47% 26-28, 32, 36, 43, 46, 49, 52, 56-68, 75-78, 81, 86, 89, 98, 110-115, 118-120, 123-127, 130-141, 146-151, 154, 158-166, 171-172, 175, 180, 185-189, 193-195, 198, 206, 209-214, 217-223, 226, 229-230, 235, 238, 241, 244, 252-254, 258, 270-278, 281-291, 296-299, 307, 331-334, 340 /usr/local/lib/python3.8/dist-packages/jedi/inference/flow_analysis.py 84 65 23% 15-20, 23-26, 29, 39-42, 46-83, 87-110, 114-123 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/annotation.py 228 193 15% 34-45, 49-59, 63-76, 88-107, 112-132, 139-182, 186-195, 204-230, 247-272, 276-282, 295-304, 308-313, 351-370, 374, 378-381, 385, 389-408, 414-425, 429-433, 437-443 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/base.py 217 149 31% 18-20, 23-32, 35, 38, 53-54, 57-63, 68, 73-81, 86, 89, 93, 96-117, 122-137, 147, 162-163, 166, 169-178, 181, 184-185, 188, 192-193, 196, 199-201, 205-242, 247-248, 252-261, 264-279, 284-293, 296, 310-314, 318, 322-332, 338, 342, 345, 348, 353-355, 359, 362, 365, 370-373, 376, 379 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/conversion.py 138 122 12% 11-47, 51-59, 64-92, 96-98, 109-142, 146-153, 158-167, 175-208 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/generics.py 67 38 43% 15-23, 28-32, 35, 40-41, 45, 48, 53-63, 70-71, 74-78, 81, 86, 89, 92, 95, 98, 101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/stub_value.py 69 43 38% 13-14, 17, 25-34, 37, 43-50, 53, 60, 65-70, 73, 78-81, 88-101 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/type_var.py 84 65 23% 9-18, 27-48, 53-71, 74, 77, 80-85, 89, 93, 98-105, 108, 111-114, 117 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typeshed.py 158 130 18% 23-26, 33-53, 57-69, 81-89, 95-124, 130-143, 154-229, 233-249, 258-264, 271, 281-295 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/typing.py 236 154 35% 40, 43-96, 105-130, 137, 140, 151, 159, 174, 179, 183, 189-228, 237-240, 244, 247, 250, 253-266, 269, 278-286, 293, 296-303, 306-310, 313-316, 321-323, 327-355, 368-369, 376, 381-386, 397-399, 402, 406-407, 413, 426-429, 433, 436-442, 445-449, 455-457 /usr/local/lib/python3.8/dist-packages/jedi/inference/gradual/utils.py 17 14 18% 11-30 /usr/local/lib/python3.8/dist-packages/jedi/inference/helpers.py 122 97 20% 17-21, 29-43, 65-109, 113-121, 125-129, 133, 137-139, 143, 147, 151, 160-163, 167-192, 196, 200-207 /usr/local/lib/python3.8/dist-packages/jedi/inference/imports.py 284 237 17% 39, 42-43, 46, 53-70, 75-96, 100-118, 122-125, 134-152, 169-224, 229, 235-238, 247-260, 269-273, 284-326, 331-356, 365-427, 432-440, 450-464, 473-510, 514-519, 523-548, 558-563 /usr/local/lib/python3.8/dist-packages/jedi/inference/lazy_value.py 37 18 51% 7-9, 12, 15, 21, 27, 32, 35, 40-44, 47-48, 52-55, 61 /usr/local/lib/python3.8/dist-packages/jedi/inference/names.py 450 314 30% 16-23, 38, 44, 47-54, 58, 61, 64, 67-69, 73, 76, 80, 87, 99-101, 104, 109-110, 113-126, 129-132, 135-139, 142-211, 214-215, 219, 223, 228, 231-237, 240, 243-245, 248-251, 255, 260-261, 264, 278-279, 287-290, 307-331, 334-346, 351-354, 357-360, 363-368, 371, 378, 381, 392, 396-401, 409-416, 419-424, 427, 432-434, 437, 441, 444-450, 453-456, 460, 463-488, 491-496, 502, 506-525, 530-531, 534-538, 541-543, 548, 551, 554, 562-563, 566-574, 578-584, 588-590, 593, 597, 600, 609, 612, 615, 620-634, 640-644, 651-652, 656 /usr/local/lib/python3.8/dist-packages/jedi/inference/param.py 130 113 13% 14-18, 23-26, 29, 32-47, 50, 73-225, 246, 250-257 /usr/local/lib/python3.8/dist-packages/jedi/inference/parser_cache.py 4 1 75% 6 /usr/local/lib/python3.8/dist-packages/jedi/inference/recursion.py 67 47 30% 55, 64-75, 81-90, 100-105, 108-109, 112-153 /usr/local/lib/python3.8/dist-packages/jedi/inference/references.py 182 157 14% 29-42, 46, 53-69, 73-77, 81-96, 100-113, 117-160, 164-175, 179-193, 197-218, 222-224, 228-245, 257-270, 274-291 /usr/local/lib/python3.8/dist-packages/jedi/inference/signature.py 108 72 33% 9-34, 39-40, 44, 48, 51-54, 57, 60, 63-65, 70-71, 74, 80-82, 86-89, 93-97, 100-117, 122-124, 128, 132-134, 137, 146, 149 /usr/local/lib/python3.8/dist-packages/jedi/inference/syntax_tree.py 545 485 11% 45-63, 69-137, 144-150, 155, 161-237, 241-261, 270-352, 357-366, 385-444, 448-469, 477-487, 493-502, 506-518, 526-538, 542, 546, 550, 554-567, 571-642, 647-730, 741-782, 789-809, 814, 821-846 /usr/local/lib/python3.8/dist-packages/jedi/inference/sys_path.py 148 125 16% 18-29, 43-72, 79-97, 105-135, 139-147, 151-170, 174-177, 189-207, 211-215, 230-271 /usr/local/lib/python3.8/dist-packages/jedi/inference/utils.py 55 23 58% 13, 20, 26, 32, 74-78, 84-86, 89, 92, 96, 99-103, 112-115 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jedi/inference/value/decorator.py 7 3 57% 11-12, 15 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/dynamic_arrays.py 114 87 24% 36-40, 52-123, 128-130, 144-145, 148-149, 152-165, 168, 173-175, 178, 181-187, 192-194, 197, 202-204 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/function.py 305 223 27% 32-33, 37, 40, 45-54, 61-64, 67-71, 74, 79-81, 84, 87, 90-114, 117-118, 121-123, 126, 132-161, 164-165, 168, 171, 176-177, 180, 185-186, 189, 194-197, 201, 206, 211-248, 251-263, 268-312, 315, 321, 327-360, 365-366, 369, 377-378, 381, 391, 394, 401, 406-407, 410-421, 424, 427, 431-470 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/instance.py 353 227 36% 28-30, 33, 36, 41-42, 45-58, 63-64, 67, 74-81, 86-87, 94-97, 100, 103, 106, 109, 113, 117, 120-121, 125, 128, 134, 137, 144-146, 149-155, 159, 162, 168-172, 176, 179-199, 203-215, 223-239, 242-250, 253-275, 278-283, 291-297, 303-307, 314-322, 328-349, 352, 355-370, 373-393, 396, 406-414, 418-422, 427-428, 431, 434, 437-438, 446-448, 451, 455, 461-462, 465-466, 469-473, 476-480, 483, 489, 492, 497, 500, 508-510, 514, 517, 522-523, 527-529, 532, 535, 545-546, 549, 552, 555, 561, 569-575, 578-580, 583-592, 595-597, 605, 608, 613-614, 617-619 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/iterable.py 404 270 33% 27, 37-40, 49-52, 55, 58, 62, 68, 71, 75, 81-82, 85, 88, 91, 95-122, 133, 136-163, 168-170, 173-174, 177, 182, 190, 193, 196-203, 206, 210, 213-215, 220-224, 231-237, 251, 255, 262-267, 270-271, 274-280, 283, 286, 290-291, 295-306, 311, 321-329, 332-334, 338-343, 350-358, 362, 365-407, 414-417, 420, 427-429, 433-439, 447-453, 457-458, 462-470, 473, 479, 490-491, 494-499, 502, 505, 508, 523-524, 527-528, 531-548, 552, 558, 561, 564, 567, 572-574, 577-579, 582, 589-620, 625-630, 633-635, 642-658 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/klass.py 204 151 26% 59-61, 66-75, 81-87, 90, 100-105, 112-126, 130-131, 136, 139-144, 147, 151, 154, 158-187, 190-221, 227-229, 232, 235-237, 243-267, 275-289, 292-296, 300-309, 314-317, 329-330, 336-356, 360-361, 365-379 /usr/local/lib/python3.8/dist-packages/jedi/inference/value/module.py 129 85 34% 22-24, 27-35, 45-56, 63-73, 76-77, 80, 83, 88, 92-98, 101-104, 111-128, 136, 144-156, 159-164, 167-169, 175-178, 181, 184-186, 194-219, 222, 225 /usr/local/lib/python3.8/dist-packages/jedi/parser_utils.py 194 159 18% 26-56, 60-69, 79, 83-94, 99-109, 113-124, 128-142, 158-175, 182-188, 196-219, 223-228, 235-244, 252-269, 280, 287-292, 299-311, 322-326 /usr/local/lib/python3.8/dist-packages/jedi/plugins/__init__.py 31 1 97% 21 /usr/local/lib/python3.8/dist-packages/jedi/plugins/flask.py 11 8 27% 7-20 /usr/local/lib/python3.8/dist-packages/jedi/plugins/pytest.py 99 71 28% 21-25, 31-41, 44-61, 67-77, 83-90, 95-98, 108-112, 117, 123-142, 147-153, 156-164 /usr/local/lib/python3.8/dist-packages/jedi/plugins/registry.py 5 0 100% /usr/local/lib/python3.8/dist-packages/jedi/plugins/stdlib.py 440 292 34% 106-132, 138-143, 157-173, 181-188, 194, 200-208, 213-217, 223-224, 227, 230-235, 238-241, 246-253, 258-259, 263, 268, 278-289, 294-328, 336, 341, 346-347, 350, 358-360, 363, 366, 371-372, 375-377, 382, 391-392, 395-397, 403, 408, 423-470, 475-478, 481-485, 488-502, 505-509, 514, 519, 524-526, 529-530, 535-537, 540-549, 553, 560, 568, 573, 581-586, 591-612, 617-618, 621, 626-628, 631, 634-637, 642-643, 647-661, 666, 675, 680-681, 685, 688, 693, 702-711, 717-734, 801-808, 814-817, 821, 824-825, 828-833, 838-842 /usr/local/lib/python3.8/dist-packages/jedi/settings.py 19 2 89% 72, 75 /usr/local/lib/python3.8/dist-packages/joblib/__init__.py 18 0 100% /usr/local/lib/python3.8/dist-packages/joblib/_compat.py 15 3 80% 11, 24-25 /usr/local/lib/python3.8/dist-packages/joblib/_memmapping_reducer.py 180 132 27% 28, 38-39, 70, 73-78, 81-95, 98, 107-119, 152-178, 183, 189-202, 213-236, 243-252, 281-286, 293-298, 301-361, 374-434 /usr/local/lib/python3.8/dist-packages/joblib/_memory_helpers.py 65 63 3% 5-105 /usr/local/lib/python3.8/dist-packages/joblib/_multiprocessing_helpers.py 34 11 68% 20-21, 34-37, 51-53, 61-64 /usr/local/lib/python3.8/dist-packages/joblib/_parallel_backends.py 271 174 36% 38-39, 78-79, 92, 99, 123, 132-136, 153, 163-184, 188, 203-205, 209-212, 216-220, 230-238, 242-245, 249, 253, 258-260, 282-284, 288-344, 348-360, 367-368, 392-399, 407-409, 432-462, 467-489, 493-497, 509-519, 523-547, 551-555, 561-564, 567-574, 579-583, 590, 593, 604, 607-624, 631 /usr/local/lib/python3.8/dist-packages/joblib/_store_backends.py 196 137 30% 26-31, 152-174, 179-193, 198-200, 205-208, 212, 217-223, 227-238, 242-243, 247-249, 253-260, 264-270, 274, 278, 282-294, 298-322, 326-328, 332, 345-348, 352, 356-388, 395-415 /usr/local/lib/python3.8/dist-packages/joblib/backports.py 48 37 23% 22-30, 37-76, 80-81 /usr/local/lib/python3.8/dist-packages/joblib/compressor.py 315 209 34% 12-13, 17-18, 22-23, 27-28, 61, 65, 73, 78, 107-110, 115, 127, 130-131, 136-140, 145-152, 164, 168-172, 178-186, 202, 206-209, 220, 225-235, 239-243, 248-249, 289-321, 330-348, 353, 357-358, 362, 366-367, 371-372, 377-383, 386-388, 391-393, 396-401, 406-424, 430-440, 446-470, 478-485, 492-493, 502-511, 515-520, 537-562, 566-568 /usr/local/lib/python3.8/dist-packages/joblib/disk.py 59 42 29% 27-38, 44-52, 59-63, 90-101, 106-124 /usr/local/lib/python3.8/dist-packages/joblib/executor.py 32 21 34% 28-50, 58-59, 63-65, 68-69, 72-73 /usr/local/lib/python3.8/dist-packages/joblib/externals/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle.py 623 480 23% 65-66, 85, 88-95, 109-115, 119-124, 128-139, 151-164, 169-202, 209-227, 257-274, 334-339, 343-384, 388, 406, 410-432, 440-443, 448-465, 473-477, 480-488, 491, 496-499, 505-509, 517-541, 551-556, 579-582, 591-608, 617-692, 706-760, 767-812, 820-847, 850-851, 854, 865-879, 886-892, 900-941, 944, 948, 953-954, 961-967, 974-989, 996-1033, 1036, 1039, 1050, 1055, 1060, 1066, 1073, 1083-1089, 1093-1094, 1109, 1122-1128, 1138-1139, 1143-1145, 1149, 1153, 1157-1161, 1186, 1194-1251, 1260, 1271-1281, 1296-1297, 1305-1315, 1335-1348, 1357-1397 /usr/local/lib/python3.8/dist-packages/joblib/externals/cloudpickle/cloudpickle_fast.py 227 169 26% 47, 60-63, 70-78, 83-84, 91, 103-132, 136-158, 162-174, 190-198, 203-208, 212-213, 218-260, 264, 268, 272, 276-279, 283, 287, 291, 295, 306-312, 320-330, 346-370, 374-385, 419-431, 463-475, 481-483, 498-501, 504-534, 537-547 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/_base.py 287 271 6% 34-615, 623-627 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/_posix_reduction.py 41 21 49% 20, 29-31, 36-43, 51-52, 55-58, 67-68, 71-74 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat.py 18 8 56% 14, 19, 23, 29-38 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/compat_posix.py 4 1 75% 11 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/context.py 135 95 30% 37-85, 91-97, 101, 118-153, 164-165, 170-171, 175-206, 214-215, 219-220, 224-225, 229-230, 234-235, 239-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/process.py 57 42 26% 20-31, 35-39, 42-64, 67-81, 89, 100-108 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/queues.py 131 102 22% 36-62, 66-67, 72-75, 79-111, 121-175, 182-183, 187-189, 195-210, 214-215, 219, 225-229, 234-240 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/reduction.py 126 59 53% 27-30, 63-66, 79-82, 87, 91, 101, 109, 113, 121, 127-129, 146, 149, 154-165, 176-177, 185-197, 202-210, 218, 223, 232-234, 240, 246-250, 256 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/backend/utils.py 94 75 20% 11-12, 20-21, 25-28, 32-46, 52-60, 67-116, 125-138, 143-145, 149-172 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/cloudpickle_wrapper.py 60 44 27% 7-8, 16-17, 20-24, 29-31, 38, 42-44, 48-49, 55-83, 95-113 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py 507 411 19% 88-89, 94, 132-138, 143, 146-147, 150-155, 158-159, 171-174, 177-179, 182-184, 189-197, 214, 217, 223-229, 232, 236-237, 245-248, 254-256, 262-268, 271-272, 275, 283-286, 289-312, 317-325, 337, 342-347, 372-465, 487-505, 542-757, 766-785, 794-797, 803-810, 882-933, 939-951, 955-994, 998-1014, 1019-1022, 1025-1048, 1073-1081, 1084-1117 /usr/local/lib/python3.8/dist-packages/joblib/externals/loky/reusable_executor.py 91 70 23% 34-37, 84-142, 150-155, 158-159, 163-194, 199-207, 212-213 /usr/local/lib/python3.8/dist-packages/joblib/format_stack.py 209 188 10% 34-35, 45-68, 72, 88-94, 104-116, 120-147, 151-176, 181-322, 337-365, 371-401 /usr/local/lib/python3.8/dist-packages/joblib/func_inspect.py 176 154 12% 47-79, 84-93, 110-162, 173-177, 190-193, 198-203, 228-318, 322-325, 330-349, 356-359 /usr/local/lib/python3.8/dist-packages/joblib/hashing.py 117 85 27% 24, 33-42, 49, 58-64, 67-75, 78-94, 101-103, 111-127, 141-150, 155, 174-182, 189-242, 258-267 /usr/local/lib/python3.8/dist-packages/joblib/logger.py 76 55 28% 28-31, 35-36, 40-44, 48-57, 77, 81, 85, 96-124, 136-156 /usr/local/lib/python3.8/dist-packages/joblib/memory.py 374 270 28% 57-63, 92-100, 106, 109, 112, 114-133, 135, 146-148, 153-160, 166-182, 225-244, 248-253, 257-277, 281, 284, 293-295, 306-307, 310-313, 316-317, 320-325, 329, 332-333, 358, 361, 365, 415-453, 483-545, 562-563, 568, 574-576, 583, 588-590, 594-595, 605-625, 636-713, 717-724, 730-745, 764-795, 804, 884, 887-901, 905, 914-921, 952, 956-962, 971-974, 978-979, 990-992, 999, 1008-1010 /usr/local/lib/python3.8/dist-packages/joblib/my_exceptions.py 53 20 62% 24, 27-33, 46-48, 51-59, 75, 80, 84, 89, 94-99, 112-113 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle.py 204 161 21% 13-14, 78-82, 91-104, 112-161, 165-178, 195-209, 234-249, 253-260, 272-295, 320-332, 342-355, 361, 415-515, 526-548, 588-607 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_compat.py 105 75 29% 21-25, 38-61, 71-75, 90-92, 96-120, 140-142, 148-154, 164-173, 176, 185-192, 198, 227-247 /usr/local/lib/python3.8/dist-packages/joblib/numpy_pickle_utils.py 92 65 29% 22-23, 27-28, 36-37, 45-49, 54-56, 73-90, 95-100, 105-112, 144-182, 187-197, 229-245 /usr/local/lib/python3.8/dist-packages/joblib/parallel.py 362 293 19% 40-41, 65-73, 83-124, 181-209, 212, 215, 218-222, 234, 241-249, 254-255, 259, 267-270, 282-291, 297-311, 327-329, 332-340, 360-363, 388-389, 620-696, 699-701, 704-705, 709-725, 728-730, 733-734, 744-759, 769-771, 783-836, 842-849, 855-887, 895-940, 943-1032, 1035 /usr/local/lib/python3.8/dist-packages/joblib/pool.py 116 83 28% 42-43, 75-87, 91-99, 120-127, 130-131, 135-137, 140, 143-177, 199-207, 210-216, 296-313, 316-329 /usr/local/lib/python3.8/dist-packages/jupyter_client/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/adapter.py 256 210 18% 17-25, 38-52, 64, 67, 70-74, 81, 84-96, 103-109, 125-127, 132-146, 149-151, 154-157, 160-170, 173-179, 182-190, 194-195, 200-202, 205-214, 219-220, 231-234, 239-258, 261-266, 269-282, 285-290, 297-307, 310-317, 321-339, 344-346, 349-358, 363-364, 386-398 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/channels.py 45 28 38% 29-32, 35-37, 41-48, 52-58, 62, 65-70, 74, 79, 82 /usr/local/lib/python3.8/dist-packages/jupyter_client/asynchronous/client.py 188 138 27% 23-29, 34, 69, 77, 81, 85, 89, 94-101, 112-150, 162-177, 194-212, 216-224, 231-235, 239-253, 311-388 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/channels.py 50 32 36% 11-12, 35-38, 41-43, 47-57, 61-67, 71, 74-79, 83, 88, 91 /usr/local/lib/python3.8/dist-packages/jupyter_client/blocking/client.py 161 121 25% 25-31, 36, 77-115, 127-142, 159-179, 183-191, 198-202, 262-339 /usr/local/lib/python3.8/dist-packages/jupyter_client/channels.py 119 82 31% 61-80, 87-88, 91-99, 109-132, 136-170, 174, 178, 182-185, 189-192, 195-200, 210 /usr/local/lib/python3.8/dist-packages/jupyter_client/channelsabc.py 25 7 72% 16, 20, 24, 37, 41, 45, 49 /usr/local/lib/python3.8/dist-packages/jupyter_client/client.py 172 120 30% 26-30, 53, 78, 82, 86, 90, 105-118, 125-134, 139, 148-155, 160-167, 172-179, 184-190, 195-202, 206-218, 258-277, 295-300, 322-329, 363-370, 379-381, 390-396, 404-406, 410-412, 420-422, 441-443 /usr/local/lib/python3.8/dist-packages/jupyter_client/clientabc.py 45 14 69% 32, 36, 40, 44, 48, 52, 60, 64, 68, 72, 76, 80, 84, 88 /usr/local/lib/python3.8/dist-packages/jupyter_client/connect.py 247 186 25% 79-167, 191-224, 253-274, 296, 319-325, 329-330, 350, 355-356, 377-396, 402-406, 413-419, 423-430, 438-446, 455-461, 465-481, 492-497, 511-523, 531-538, 542-551, 555-557, 561, 565, 569, 573 /usr/local/lib/python3.8/dist-packages/jupyter_client/jsonutil.py 50 34 32% 38-44, 53-59, 63-72, 76-84, 88-92 /usr/local/lib/python3.8/dist-packages/jupyter_client/kernelspec.py 196 139 29% 44-47, 50-58, 65, 73, 82, 90-103, 108, 111, 130, 134, 146-159, 163-183, 190-200, 204-222, 229-237, 252-270, 277-289, 292-297, 315-347, 351-354, 359, 366, 370, 376 /usr/local/lib/python3.8/dist-packages/jupyter_client/launcher.py 59 51 14% 56-158 /usr/local/lib/python3.8/dist-packages/jupyter_client/localinterfaces.py 170 129 24% 29-30, 34-42, 48-52, 58-59, 68-89, 96-108, 113-121, 127-135, 140-164, 173-196, 201-203, 216-248, 254, 259, 264, 269, 274 /usr/local/lib/python3.8/dist-packages/jupyter_client/manager.py 349 261 25% 42, 48, 52, 61, 64, 75-77, 83-85, 102, 109, 113, 126-127, 134, 137, 141-143, 147-149, 157-166, 174-205, 212, 217-219, 222-225, 239-267, 274-283, 286-287, 301-306, 311-315, 323-338, 342-347, 368-379, 406-418, 423, 430-456, 464-478, 488-498, 502-509, 523-524, 538-543, 551-562, 583-594, 621-634, 641-675, 683-697, 707-717, 721-728, 735-736, 744-755, 760-771, 784-789 /usr/local/lib/python3.8/dist-packages/jupyter_client/managerabc.py 27 8 70% 21, 29, 33, 37, 41, 45, 49, 53 /usr/local/lib/python3.8/dist-packages/jupyter_client/multikernelmanager.py 185 104 44% 31-39, 60-63, 67, 73, 76-90, 94-105, 109, 119, 123, 126, 130-147, 157-160, 174-188, 198, 212, 216-222, 233, 247, 258, 275-276, 286-287, 402, 422-428, 442-456, 461-463, 473-475, 488-490, 500-502, 506-512 /usr/local/lib/python3.8/dist-packages/jupyter_client/session.py 442 311 30% 27, 65-75, 148-153, 157, 172, 177, 184, 187-191, 201-205, 209, 212, 215, 218, 221, 226-228, 232-247, 305-315, 323-333, 338-340, 344, 364, 368, 376-384, 388, 393-396, 411-412, 423-425, 432-434, 488-496, 508-514, 519-521, 525-568, 571, 580-590, 600-605, 630-664, 711-766, 784-794, 810-828, 853-865, 869-876, 883-889, 916-954, 957-961, 965-978 /usr/local/lib/python3.8/dist-packages/jupyter_core/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/jupyter_core/paths.py 194 155 20% 33-38, 47-51, 59-68, 78-98, 109-114, 118-122, 147-166, 170-174, 186-202, 209-213, 232-247, 266-287, 291, 316-344, 361-377, 394, 415-448, 452-456 /usr/local/lib/python3.8/dist-packages/jupyter_core/version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/__init__.py 18 5 72% 22, 37-40 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/affine_transformations.py 107 77 28% 16-17, 22-24, 28-31, 55-59, 84-90, 114-118, 145-156, 171-180, 194-195, 212-219, 236-242, 246-251, 281, 285-289, 292-298, 301-308, 311-317, 320-335 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py 120 97 19% 93-99, 124-173, 180-223, 227-233, 237-261, 273-284, 288, 292-295, 299 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/directory_iterator.py 65 4 94% 72-73, 106, 162 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/image_data_generator.py 213 111 48% 17-18, 301, 308-310, 316, 327-331, 335-342, 347-349, 354-356, 361-363, 421, 649-666, 708, 712, 714, 717-720, 725-728, 734-739, 761, 764, 771-778, 783-790, 795, 804, 814, 819, 877, 882, 885, 888, 902-903, 933-988 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/iterator.py 146 51 65% 50, 54, 59, 74, 78-95, 101, 104, 112-116, 127, 175, 180-183, 188-193, 201, 205-209, 243-250, 253, 255-257, 263-268, 272, 277, 285, 292 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/numpy_array_iterator.py 87 75 14% 46-52, 68-150, 156-183 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/utils.py 121 54 55% 16-18, 47, 71-76, 107-109, 111, 118-119, 121-122, 125-127, 132, 153-154, 179, 217, 251-286, 305, 312-319 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/sequence.py 155 135 13% 55-110, 143-149, 197-240, 254-259, 334-354, 360, 364-378, 386-402, 427-432, 446-454 /usr/local/lib/python3.8/dist-packages/keras_preprocessing/text.py 198 166 16% 21, 42-63, 88, 128-138, 179-197, 212-251, 263-267, 281, 298-324, 338, 355-370, 382-383, 400-438, 449-455, 482-487, 500-519 /usr/local/lib/python3.8/dist-packages/parso/__init__.py 8 3 62% 56-58 /usr/local/lib/python3.8/dist-packages/parso/_compatibility.py 36 15 58% 27-29, 38-43, 49-51, 60-64, 69 /usr/local/lib/python3.8/dist-packages/parso/cache.py 103 67 35% 66, 68, 89-94, 101-111, 120-144, 148-160, 164-174, 178-179, 183-186, 190-193, 197-202 /usr/local/lib/python3.8/dist-packages/parso/file_io.py 20 11 45% 6, 12-13, 19-23, 26, 31-32, 35 /usr/local/lib/python3.8/dist-packages/parso/grammar.py 127 89 30% 33-40, 77-79, 90-155, 158-161, 169-172, 175, 178-183, 190-191, 194-196, 199-201, 210-216, 219, 223, 234-260 /usr/local/lib/python3.8/dist-packages/parso/normalizer.py 137 74 46% 19-24, 27-33, 36-39, 42-48, 52-53, 56-57, 60-65, 68, 71, 74-77, 101, 117-120, 125-138, 141, 144, 147, 150, 158, 161, 164, 167-171, 174-181, 184-186, 191, 194-197, 200-203 /usr/local/lib/python3.8/dist-packages/parso/parser.py 106 79 25% 37-38, 49-54, 59-71, 76-77, 81, 84, 89-96, 119-121, 124-143, 146-151, 154-160, 163-166, 174-198, 201-211 /usr/local/lib/python3.8/dist-packages/parso/pgen2/__init__.py 1 0 100% /usr/local/lib/python3.8/dist-packages/parso/pgen2/generator.py 178 152 15% 45-47, 56-57, 60, 74-87, 90-93, 96-98, 102-112, 117, 130, 133, 145-157, 170-207, 211-224, 228-232, 244-274, 282-294, 303-344, 352-377 /usr/local/lib/python3.8/dist-packages/parso/pgen2/grammar_parser.py 101 81 20% 18-23, 27-38, 42-58, 62-69, 73-94, 98-111, 115-122, 125-126, 129-135, 141-142, 145, 150-151, 154-156, 159 /usr/local/lib/python3.8/dist-packages/parso/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/parso/python/diff.py 502 437 13% 52, 56-58, 62-64, 68, 72, 81-118, 122-138, 142-146, 154-167, 171-173, 177-184, 192-195, 199-203, 207-220, 224-230, 238-247, 256-258, 261-264, 282-340, 343-344, 347-388, 391-405, 412-431, 439-453, 456-514, 523-527, 530-550, 553, 557-562, 565-586, 589, 594-599, 603, 606-609, 612-624, 627-640, 646-665, 672-685, 693-719, 723-861, 864-886 /usr/local/lib/python3.8/dist-packages/parso/python/errors.py 758 567 25% 26-33, 37-48, 56-57, 65-72, 76, 83-93, 97-121, 125-131, 135-136, 141-148, 153, 157, 160-170, 176-202, 205-247, 251-253, 256, 259, 267-269, 272-282, 285-291, 295-315, 318-349, 352, 355, 360-362, 365-368, 375-376, 384-385, 389, 400-401, 409, 413, 421, 425, 433-437, 446-456, 464, 467, 477-483, 491-522, 530-535, 546-548, 558, 561-570, 579-581, 584-590, 599-600, 608, 616-633, 643-661, 667-693, 703, 706-730, 736-750, 758, 765-768, 771-823, 833-845, 853-860, 871, 874-889, 892, 895-897, 902-983, 991-995, 1004-1009, 1015, 1021-1024, 1030-1031, 1038-1040, 1049-1108 /usr/local/lib/python3.8/dist-packages/parso/python/parser.py 102 85 17% 66-71, 74-80, 90-108, 112-118, 121-188, 191-200, 203-217 /usr/local/lib/python3.8/dist-packages/parso/python/pep8.py 492 426 13% 39-40, 43, 46-51, 56-105, 114-119, 126-144, 148, 153-172, 176-178, 182-280, 283-286, 289-290, 293-341, 344-375, 378-519, 522-537, 545-629, 632-685, 688-700, 727 /usr/local/lib/python3.8/dist-packages/parso/python/prefix.py 56 35 38% 11-16, 20-25, 28-29, 35, 69-94 /usr/local/lib/python3.8/dist-packages/parso/python/token.py 12 1 92% 10 /usr/local/lib/python3.8/dist-packages/parso/python/tokenize.py 458 395 14% 60-61, 70, 75, 80-111, 119-124, 136-265, 274-278, 283, 289-295, 298, 301-304, 307, 310, 313, 317-331, 335-371, 376-377, 384-389, 401-672, 676-705, 709-722 /usr/local/lib/python3.8/dist-packages/parso/python/tree.py 642 403 37% 48-49, 79-97, 111-119, 126, 134-143, 154, 180, 192, 204, 211, 221-249, 267, 270-275, 311-314, 318, 321, 343, 349, 355, 361, 364-372, 378, 381-386, 400-401, 412-417, 426-429, 436-453, 469, 475-485, 496, 503-509, 523-566, 586-588, 591, 597, 601, 607-624, 630-639, 645-654, 660, 667-673, 692-694, 701, 704, 711, 714, 734-736, 744-752, 758-763, 779, 782, 794-798, 810-815, 818-821, 833-842, 845, 848, 861, 865, 869-877, 882-888, 891-905, 914-918, 932, 937, 940, 944-960, 969, 976, 996, 1000, 1003-1008, 1016, 1023, 1040-1061, 1072-1075, 1084-1090, 1096-1105, 1117-1120, 1128-1131, 1139-1144, 1152-1159, 1165-1166, 1173-1176, 1179, 1186-1201, 1207, 1216-1222, 1228-1229, 1241, 1254, 1257, 1260, 1263, 1266, 1270 /usr/local/lib/python3.8/dist-packages/parso/tree.py 195 124 36% 18-20, 38-41, 48-58, 66-75, 82-101, 108-127, 186-197, 203, 207-208, 211-216, 219, 222, 225-228, 232-239, 243-246, 253-254, 266-271, 278, 281, 285, 288-292, 295, 306-328, 331, 334, 338-341, 350-351, 354, 376-377, 380 /usr/local/lib/python3.8/dist-packages/parso/utils.py 86 68 21% 38-68, 80-109, 117-119, 123-140, 146-152, 155-159, 162, 171-176 /usr/local/lib/python3.8/dist-packages/pickleshare.py 194 152 22% 43-45, 50-51, 62, 65, 73-86, 91-107, 111-123, 127-133, 139-154, 158-177, 186-198, 204-211, 215, 220-224, 227, 230, 240-243, 259-275, 279, 282, 297, 300, 302, 304-306, 311-347, 350 /usr/local/lib/python3.8/dist-packages/pooch/__init__.py 20 14 30% 36-50 /usr/local/lib/python3.8/dist-packages/pooch/_version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/pooch/core.py 120 84 30% 197-230, 410, 413, 424, 545-568, 575-576, 589-590, 610-637, 658-673, 702-711, 725-733 /usr/local/lib/python3.8/dist-packages/pooch/downloaders.py 82 69 16% 14-15, 47-60, 139-143, 161-203, 253-261, 277-310 /usr/local/lib/python3.8/dist-packages/pooch/processors.py 75 50 33% 38, 64-81, 88, 117-133, 162-182, 213, 240-252, 262-279 /usr/local/lib/python3.8/dist-packages/pooch/utils.py 101 54 47% 33, 96, 150, 173-193, 214-216, 246, 248, 255-256, 261-271, 308-315, 346-362, 386-393, 430-436 /usr/local/lib/python3.8/dist-packages/pooch/version.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/application.py 442 346 22% 93-94, 220-336, 343-353, 366-374, 385, 395-399, 410-431, 437-477, 482, 492-523, 530-537, 546-559, 569, 579-581, 585-591, 614-778, 801-811, 826-839, 849-851, 862-869, 875-884, 916-929, 938-939, 959-981, 993-1006, 1019, 1030, 1034-1036, 1043-1053, 1064-1065, 1073, 1078, 1087-1132, 1136-1140, 1145, 1148, 1158-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/current.py 64 39 39% 7-8, 11-13, 50, 54-58, 62-66, 75, 97-103, 111-112, 127-134, 148-165 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/dummy.py 17 5 71% 21, 28, 35, 44, 47 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/application/run_in_terminal.py 44 33 25% 13-14, 50-57, 73-116 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/auto_suggest.py 57 28 51% 23, 44, 47, 82, 93, 98, 107-110, 121, 132-145, 155-156, 161-164, 175, 180-181, 186-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/buffer.py 775 644 17% 82-90, 93, 106-108, 114-128, 136-138, 153-155, 158, 251-316, 319-324, 332-383, 389-405, 409-412, 416, 426-442, 446, 453-464, 468, 472-478, 482-498, 504-516, 524, 536, 557-571, 578, 589-596, 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44-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/base.py 116 71 39% 48-62, 65-72, 80-82, 90, 95-97, 102-104, 109-111, 120-122, 147-153, 156, 185, 196-197, 212, 217, 225-228, 231, 242, 245, 256, 261-262, 267-272, 275, 284, 290-292, 299-301, 308, 318-333, 338-349 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/filesystem.py 46 36 22% 34-38, 43-98, 107 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/fuzzy_completer.py 70 52 26% 54-60, 65-68, 71-75, 81-116, 131-159, 180-188, 193 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/nested.py 40 28 30% 32-33, 36, 63-75, 81-109 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/completion/word_completer.py 33 26 21% 41-49, 55-83 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/data_structures.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/document.py 525 407 22% 52, 68-71, 98-121, 130, 133-136, 145, 150, 155, 160, 165, 169, 173, 178-179, 184-185, 193-196, 205-224, 231, 237, 243, 248-250, 256-259, 266, 273, 280-281, 291-292, 301-304, 312-315, 324-340, 345, 350, 356, 372-397, 404-405, 420-434, 446-453, 458-461, 475-496, 510-536, 546-547, 556-572, 581-604, 613-625, 634-652, 661-671, 680-690, 696-699, 705-708, 720-725, 742-747, 761-783, 794-816, 828-834, 838, 842, 846-854, 858, 864, 872-876, 890-897, 908-947, 958-997, 1005-1033, 1048-1098, 1104-1111, 1118-1129, 1136-1145, 1154, 1165-1174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/enums.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/async_generator.py 26 18 31% 32-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/inputhook.py 65 42 35% 50-52, 61-63, 78-80, 83, 86, 89, 94-143, 149-154, 157, 166-167, 170 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/eventloop/utils.py 37 25 32% 9-10, 30-33, 57-83, 90-100 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/app.py 162 97 40% 13, 54-98, 106, 114, 122-123, 131-132, 140, 148, 154, 160, 168, 182, 191-195, 200, 205, 213-225, 234-248, 253-267, 272-286, 291-295, 300-304, 309-313, 319-323, 329, 334-341, 346-347, 354-355, 364-365, 371-375, 383 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/base.py 92 37 60% 20, 26, 38, 49, 67-77, 88, 90, 101-103, 117-123, 126, 129, 142, 147, 150, 159, 162, 165, 174, 177, 186, 189, 210, 213 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/cli.py 27 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/filters/utils.py 14 2 86% 32, 41 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/ansi.py 152 133 12% 30-47, 53-109, 115-187, 193-211, 214, 217, 225-228, 246 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/base.py 66 49 26% 6, 30-37, 68-99, 108-114, 126, 129, 144-145, 148-160, 168-174 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/html.py 70 59 16% 30-96, 99, 102, 110-113, 119-123, 129-132 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/pygments.py 14 6 57% 8, 22, 25-30 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/formatted_text/utils.py 24 16 33% 28-29, 40-41, 56-57, 68-85 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/history.py 95 61 36% 33-34, 64-74, 80, 84-85, 101, 120-123, 126-139, 142, 145, 150, 153, 156, 165, 168, 177, 180, 184, 193-194, 197-221, 225-232 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/ansi_escape_sequences.py 12 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/base.py 53 14 74% 48, 52, 60, 65, 95, 104, 107, 110, 114, 117, 120, 123, 126, 131 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/defaults.py 24 17 29% 26-43, 51-58 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/typeahead.py 17 8 53% 54-55, 64-67, 75-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/input/vt100_parser.py 98 73 26% 48-61, 87-88, 91-92, 98-99, 108-118, 124-168, 176-188, 199-226, 240, 246-247 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/auto_suggest.py 30 22 27% 26-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/basic.py 157 147 6% 27, 31-248 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/completion.py 96 79 18% 21-22, 37-43, 61-79, 90-171, 178-203 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/cpr.py 12 6 50% 14-28 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/emacs.py 270 255 6% 41-335, 339-401, 409-558 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/focus.py 7 2 71% 16, 24 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/mouse.py 63 54 14% 21-146 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/named_commands.py 279 156 44% 56-59, 73-74, 82-83, 91-92, 102-103, 111-112, 118-119, 128-132, 141-145, 153, 162, 176, 184, 192, 200, 208-210, 219-223, 236, 244-246, 254-262, 270, 281-289, 297-302, 310-315, 323-328, 337, 354-364, 373-382, 391-411, 420, 428-436, 444-452, 460, 471-472, 481-482, 491-498, 511, 520, 528, 541, 550, 566-569, 578-586, 599, 609-627, 635, 643, 657, 666-678, 686-687 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/open_in_editor.py 14 7 50% 20, 29-35, 42-46 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/page_navigation.py 26 19 27% 38, 51-59, 67-79 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/scroll.py 80 69 14% 26-50, 57-80, 87, 94, 101-113, 120-144, 151-160, 169-187 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/bindings/vi.py 949 904 5% 74-76, 80-85, 91-94, 105-126, 133-141, 147-166, 181-289, 304-369, 388-2161, 2165-2210 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/defaults.py 11 2 82% 35-49 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/digraphs.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/emacs_state.py 17 7 59% 18-19, 22, 27, 31, 35-36 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_bindings.py 215 138 36% 57, 101, 104, 126, 137, 149, 158, 189-196, 199-201, 205, 209, 238-281, 297-325, 340-364, 376-389, 397-417, 461-462, 469, 475-476, 480-481, 484-485, 488-489, 514-517, 521-541, 555-556, 563-572, 583, 596-599, 602-607, 617-618, 625-635 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/key_processor.py 238 184 23% 23-24, 44-47, 53, 56-58, 91-98, 101-117, 124-127, 134-145, 152-204, 213-216, 222-225, 236-292, 298-303, 306-354, 362-375, 382-387, 397-420, 443-451, 454, 462, 466-469, 476, 483, 490-499, 506, 514-525, 530 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/key_binding/vi_state.py 47 27 43% 7-8, 28-29, 40-76, 81, 86-91, 98-106 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/keys.py 165 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/containers.py 956 776 19% 62-64, 140, 148, 155, 160-166, 174, 215-227, 230, 233, 236, 289-308, 311-318, 321-327, 330-331, 339-364, 381-414, 428-474, 526-545, 548-555, 558-579, 582-583, 591-616, 623-667, 684-730, 769-775, 778-781, 784, 792, 803-841, 867-986, 1000-1011, 1014, 1017, 1020-1022, 1069-1090, 1093-1095, 1098-1100, 1103, 1149-1162, 1166, 1177-1185, 1195-1202, 1221, 1230-1236, 1243-1246, 1252-1255, 1263, 1277, 1284, 1294, 1301, 1309-1312, 1319-1324, 1342-1345, 1349, 1353, 1357, 1361, 1364, 1378-1379, 1490-1524, 1527, 1530-1543, 1551-1558, 1564, 1573-1593, 1604-1620, 1637-1671, 1680-1684, 1688-1695, 1710-1726, 1738-1898, 1924-2140, 2150-2162, 2169-2181, 2188-2191, 2203-2214, 2224-2262, 2277-2281, 2287-2292, 2303-2413, 2424-2507, 2525-2528, 2532-2541, 2545-2558, 2561, 2564, 2578-2579, 2582, 2585, 2588-2591, 2594-2597, 2608-2609, 2614, 2626, 2635-2636, 2639, 2642, 2645, 2648, 2651, 2656, 2663, 2670-2675, 2682-2687, 2695-2699 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/controls.py 311 237 24% 51-58, 88, 91, 100, 106, 126, 154, 179-186, 190-193, 216-266, 323-341, 344, 347, 350, 358, 367-369, 379-380, 384-430, 442-469, 472, 475, 488-491, 496, 545-574, 577, 583-589, 593-596, 606-610, 613, 626, 638-657, 666-670, 681-722, 730-819, 825-883, 886-887, 890-891, 897, 904-908, 929-939 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dimension.py 91 53 42% 44, 56, 58, 60, 71, 75, 78, 94, 98, 101-111, 118-122, 130-167, 184-193, 201-207 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/dummy.py 17 7 59% 26-37 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/layout.py 188 143 24% 43-70, 73, 79-81, 84-85, 101-166, 175-193, 200, 207-212, 217, 222, 227, 236-241, 248-250, 258-259, 266-269, 276-280, 289-290, 297-300, 306-307, 313-323, 329-339, 345-346, 355-360, 366-375, 382-384, 391-394, 406-417 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/margins.py 119 90 24% 18, 43, 63, 80-81, 84-85, 90-132, 141-142, 145-148, 153-156, 173-175, 178, 183-243, 276-277, 282-283, 288-305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/menus.py 281 228 19% 43-45, 71, 74-81, 91-95, 101-132, 138, 144, 159-167, 173-181, 190-205, 218-232, 245-265, 281-284, 327-340, 343, 346, 353-371, 383-390, 396-507, 513, 519-557, 564-607, 626-664, 680-685, 694, 697-702, 705-720 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/mouse_handlers.py 12 4 67% 18-24, 39-40 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/processors.py 378 291 23% 38, 80, 109-115, 122, 155-157, 168, 187, 193-243, 264-267, 278-312, 323, 326-334, 354-357, 368-398, 404-439, 451-489, 502-503, 509-522, 529, 541, 544-550, 557, 570-571, 575-580, 583, 593, 597-607, 622-629, 632-645, 660-667, 670-684, 707-710, 713-764, 790-798, 803-855, 858-903, 930-931, 937-940, 943, 958, 961-962, 969-975, 985, 988-1029 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/screen.py 100 71 29% 9, 107-120, 123, 128, 131, 152-192, 198, 204, 211-214, 221-227, 234, 242-249, 256-263, 272-293, 300-307, 310 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/layout/utils.py 32 17 47% 23, 26, 29, 35, 39, 48-53, 67-76 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/base.py 31 15 52% 38, 50, 53-62, 73-74, 77-78, 81-82 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/lexers/pygments.py 112 79 29% 29, 69, 86, 94-110, 117-129, 141-143, 193-202, 214-222, 229-335 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/mouse_events.py 14 3 79% 41-42, 45 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/base.py 130 33 75% 164, 174, 177, 180, 183, 186, 189, 192, 195, 198, 201, 204, 207, 210, 213, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/color_depth.py 29 12 59% 52-74 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/defaults.py 27 20 26% 31-62 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/output/vt100.py 289 192 34% 123-145, 169-175, 181-190, 237-256, 273, 276-300, 304-312, 321-369, 383-396, 420-432, 449-473, 476, 480, 484, 490, 497, 503-507, 512, 519, 522, 525, 528-534, 540-542, 548, 555, 558, 567-570, 573, 576, 579, 582, 588, 591-596, 599-606, 609-614, 617-622, 625, 628, 634-673, 679-680, 684-685 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/patch_stdout.py 61 43 30% 52-68, 81-93, 100-116, 128-139, 142-144, 147-150, 156-157, 164, 167 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/renderer.py 299 255 15% 27-28, 70-243, 262-263, 266-270, 303-326, 331-369, 377, 386, 395-402, 417-457, 464-480, 488, 494-514, 525-637, 648-657, 664-673, 686-716 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/search.py 86 66 23% 16-17, 56-58, 61, 73-78, 94-119, 126-150, 157-181, 190-217, 226 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/selection.py 19 5 74% 48-50, 53, 56 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/dialogs.py 89 60 33% 53-69, 86-99, 116-140, 152-159, 176-196, 213-231, 246-285, 290-294, 305 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/base.py 177 119 33% 58-59, 74-85, 128-148, 152-233, 237-247, 265-269, 272, 281-283, 286-300, 303, 306, 326-342, 345-358, 366-367, 379, 383-387, 405, 409-415, 419-422, 429-432, 439-444 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/progress_bar/formatters.py 158 91 42% 22, 52, 55, 64, 72, 75, 89-90, 93-94, 103-113, 116-124, 141, 144, 164-172, 180-206, 211, 228, 233-237, 244-247, 262-263, 268-273, 291-297, 300-306, 325-326, 329-335, 352-353, 358, 391, 402-413, 416, 423 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/prompt.py 449 368 18% 133, 159-184, 193, 406-458, 470-475, 481-490, 516, 522-685, 693-761, 767-824, 912-994, 1010-1040, 1091-1173, 1178-1189, 1193, 1197, 1202-1219, 1222, 1235-1249, 1262-1270, 1274-1282, 1286-1292, 1303, 1307, 1359-1361, 1412-1435, 1442-1443 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/shortcuts/utils.py 59 41 31% 27, 96-147, 160-173, 180-183, 190-191, 198 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/base.py 37 10 73% 129, 148, 151, 155, 166-167, 172-174, 177, 181 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/defaults.py 15 2 87% 213, 223 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/named_colors.py 3 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/pygments.py 18 11 39% 14-15, 39-43, 51-56, 66-67 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style.py 165 126 24% 39-74, 97-103, 112-167, 201, 227-242, 246, 256-264, 272-313, 316, 329-336, 352-353, 373-374, 380-383, 387-390, 395, 398 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/styles/style_transformation.py 124 76 39% 54, 80-83, 94, 111-112, 115-121, 124, 159-160, 163-189, 196-205, 220, 223, 236, 240, 256, 259-262, 265-268, 280-281, 284-286, 289, 294, 297-299, 302, 311, 349-375 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/utils.py 115 73 37% 64-68, 72-73, 77, 86, 92-93, 99-100, 106-107, 116, 119, 141-160, 170, 178, 185, 193-195, 202, 209, 214, 235-271, 276-279, 284-287, 295-298, 308-311 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/validation.py 66 34 48% 32-34, 37, 65, 73-76, 100, 112-114, 117, 120-126, 137, 140, 147-150, 159, 169-170, 174-175, 186, 189-190, 193-194 /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/prompt_toolkit/widgets/base.py 292 180 38% 192-256, 274, 278, 285, 289, 296, 300, 303, 326-342, 351, 367-382, 393-402, 411-419, 422, 448-486, 516, 529, 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/usr/local/lib/python3.8/dist-packages/pyasn1/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/compat/binary.py 18 15 17% 10-31 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/calling.py 7 3 57% 13-16 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/dateandtime.py 9 3 67% 16-17, 22 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/integer.py 68 58 15% 14-15, 20-94, 99, 102-107 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/octets.py 22 10 55% 10-27 /usr/local/lib/python3.8/dist-packages/pyasn1/compat/string.py 11 8 27% 11-21, 26 /usr/local/lib/python3.8/dist-packages/pyasn1/debug.py 85 31 64% 52, 55, 63-65, 72-101, 104, 107, 110, 113, 122, 138, 151, 154 /usr/local/lib/python3.8/dist-packages/pyasn1/error.py 10 3 70% 47-49 /usr/local/lib/python3.8/dist-packages/pyasn1/type/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/pyasn1/type/base.py 249 74 70% 65, 70, 80, 108, 132, 138-141, 144, 149, 152, 155, 158, 162, 214, 233-236, 239, 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/usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/base.py 99 80 19% 7-23, 118-151, 155-158, 170-191, 201-209, 212, 215, 231-234, 250-253, 256, 266-267, 270-275 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/bdf.py 245 222 9% 21-26, 31-34, 39-70, 188-242, 245-295, 298-438, 441, 447-451, 454-467 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/common.py 214 189 12% 13-17, 22-24, 39-40, 46-57, 62, 100-120, 153-176, 181-190, 206-238, 292-320, 325-363, 367-432 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/dop853_coefficients.py 153 0 100% /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/ivp.py 162 143 12% 31-51, 77-78, 109-128, 146-154, 504-662 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/lsoda.py 57 46 19% 108-138, 141-161, 164-172, 177-180, 183-188 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/radau.py 262 230 12% 88-137, 169-177, 286-334, 337-387, 390-529, 532-533, 536, 541-545, 548-562 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ivp/rk.py 190 127 33% 62-72, 89-104, 107, 110, 113-177, 180-181, 480-485, 488-494, 497-503, 506-524, 529-533, 536-549, 554-557, 560-576 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_ode.py 507 401 21% 348-353, 357, 361-369, 382-394, 422-437, 441-445, 532-536, 540-541, 545-546, 562-567, 584-587, 620-625, 628-633, 637-656, 660, 673-686, 690-694, 722-723, 739-742, 751-754, 765-769, 786-787, 790-791, 804, 809, 814, 826-831, 862-881, 912-939, 942-987, 990-1014, 1017-1021, 1024-1028, 1044-1100, 1132-1143, 1146-1151, 1154-1168, 1171-1179, 1182-1187, 1210, 1216-1230, 1267-1283, 1287-1333, 1336-1353, 1356-1360, 1363-1367 /usr/local/lib/python3.8/dist-packages/scipy/integrate/_quad_vec.py 262 227 13% 16, 19-24, 28, 36-41, 44-48, 51-56, 64-67, 70-71, 74-79, 83, 87-93, 98-99, 102, 202-400, 404-429, 436-449, 461-504, 512-569, 580-622 /usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py 32 22 31% 229-260 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadpack.py 199 172 14% 41, 334-432, 436-465, 469-516, 581-585, 665-674, 799-810, 815, 823, 828, 832, 837-844, 847-883 /usr/local/lib/python3.8/dist-packages/scipy/integrate/quadrature.py 312 274 12% 45-49, 117-123, 151-168, 235-252, 256-258, 315-350, 354-381, 454-506, 568-623, 650-660, 668-669, 674-686, 773-806, 934-975 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_bsplines.py 324 291 10% 20-22, 27-30, 38-43, 182-226, 235-239, 245, 303-308, 332-355, 358, 367-370, 391-397, 425-437, 488-571, 581-588, 593, 597-604, 608-617, 735-861, 969-1022 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_cubic.py 259 230 11% 28-72, 142-158, 235-240, 245-255, 268-304, 343-350, 405-435, 438, 445, 450, 619-770, 784-837 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_fitpack_impl.py 413 388 6% 44-48, 215-311, 443-524, 580-607, 654-668, 710-733, 773-791, 890-988, 1039-1057, 1079-1080, 1128-1146, 1196-1229, 1288-1311 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/_pade.py 27 22 19% 46-67 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack.py 65 49 25% 156-158, 289-290, 353-368, 417-429, 477-491, 531-534, 586-601, 654-657, 719-722 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/fitpack2.py 366 304 17% 171-196, 201-208, 211-232, 235-241, 244-256, 265-276, 303-317, 324-326, 330-332, 342, 380, 405-408, 415-421, 468-471, 524-525, 602-618, 741-770, 793, 801, 805, 841-880, 955-961, 983, 1002-1004, 1048-1069, 1110-1136, 1172-1198, 1273-1281, 1304, 1385-1396, 1486-1504, 1670-1727 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/interpolate.py 920 819 11% 32-34, 84-94, 201-252, 279-313, 318-328, 335, 431-540, 547, 552-581, 585, 591-614, 623-631, 637-646, 649, 652-654, 660-669, 687-700, 708-751, 754-758, 770-777, 784-787, 808-870, 902-923, 986, 1013-1031, 1063-1085, 1108-1167, 1217-1239, 1268, 1284-1296, 1313-1332, 1422, 1443-1477, 1502-1533, 1558-1597, 1600-1603, 1621-1639, 1705-1757, 1814-1843, 1873-1883, 1946-1965, 1978-1984, 1987-1991, 1994-1997, 2028-2062, 2069-2096, 2103-2136, 2163-2169, 2197-2203, 2236-2264, 2287-2312, 2415-2452, 2468-2501, 2505-2516, 2519-2521, 2525-2541, 2601-2676, 2689-2704, 2707, 2710-2712, 2717-2724 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/ndgriddata.py 47 38 19% 59-65, 77-81, 193-228 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/polyint.py 203 169 17% 18, 56-60, 78-80, 86, 90-92, 96-103, 106-111, 114-131, 134-139, 177-188, 216-218, 293-316, 319-326, 329-355, 400-406, 448-461, 502-513, 532-537, 559-577, 599, 602-617, 666 /usr/local/lib/python3.8/dist-packages/scipy/interpolate/rbf.py 105 83 21% 145, 148, 151, 154, 157, 160, 163, 167-216, 223-268, 274-275, 278, 281-290 /usr/local/lib/python3.8/dist-packages/scipy/io/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/_fortran.py 79 61 23% 111-126, 129-136, 161-169, 245-294, 317, 340, 349, 352, 355 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/_fortran_format_parser.py 165 124 25% 34, 62-66, 69-71, 74-80, 84-90, 94, 123-132, 141-144, 147-153, 157-163, 167, 172-174, 177, 180, 185-186, 189-191, 194-205, 234, 237-250, 253-257, 260-261, 264-305, 308-312 /usr/local/lib/python3.8/dist-packages/scipy/io/harwell_boeing/hb.py 265 215 19% 45, 70-120, 141-205, 217-281, 285-299, 303-308, 313-332, 336-360, 391-400, 403-412, 416, 421, 437-443, 447, 451, 455, 459, 463, 466, 469, 496-504, 534-547 /usr/local/lib/python3.8/dist-packages/scipy/io/idl.py 429 382 11% 81-84, 89-90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 152-153, 163-170, 175-182, 187-224, 233-269, 278-316, 322-425, 431-445, 451-492, 498-546, 552-565, 570-631, 652, 655, 658, 705-873 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/byteordercodes.py 19 12 37% 57-69 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio.py 65 47 28% 19-22, 32-47, 71-80, 215-225, 266-279, 314-317 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio4.py 281 222 21% 91-95, 102-106, 110-127, 135-149, 168-185, 202-207, 221-223, 254-267, 273-301, 313-314, 317-326, 333-334, 351-357, 375, 386-406, 410-422, 442-446, 451-452, 455, 458, 478-490, 504-519, 522-541, 544-560, 567-584, 590-594, 616-618 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5.py 371 312 16% 151-167, 172-175, 179-186, 196-198, 217-233, 252, 261-307, 311-331, 375-400, 422-455, 471-477, 480, 483, 487-496, 500-504, 508-516, 536-559, 562-570, 587-590, 601-630, 633-655, 660-698, 703-718, 721-726, 729-733, 736-738, 742-755, 761-765, 789-798, 802-809, 829-849 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/mio5_params.py 77 11 86% 195-197, 227-231, 235, 242-243, 249-250 /usr/local/lib/python3.8/dist-packages/scipy/io/matlab/miobase.py 108 70 35% 20, 179-184, 221-241, 305-318, 324, 328, 332, 363-377, 381-383, 387, 390-393, 398, 403-415 /usr/local/lib/python3.8/dist-packages/scipy/io/mmio.py 444 375 16% 54, 75, 101, 115, 119, 123, 127, 131, 135, 139, 150-151, 164-165, 178-179, 191, 196, 226-269, 294-327, 332-384, 389, 398, 417-425, 451-460, 469-479, 483-485, 490-657, 662-802, 812-828, 833-839 /usr/local/lib/python3.8/dist-packages/scipy/io/netcdf.py 486 393 19% 239-284, 289-293, 297-319, 323, 326, 348-352, 385-397, 408-409, 413-421, 425-428, 431-439, 442, 445-452, 455-479, 482-512, 515-553, 556-558, 561-602, 606-616, 619, 622-631, 634-635, 638-647, 650-733, 736-758, 761-776, 779-782, 785, 789, 793, 796, 799-802, 805-808, 867-876, 881-885, 896, 905, 919, 938-946, 958, 970, 973-988, 991-1021, 1027-1028, 1037-1045, 1058-1065, 1077-1093 /usr/local/lib/python3.8/dist-packages/scipy/linalg/__init__.py 36 4 89% 222-223, 227-228 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_ldl.py 85 74 13% 123-156, 207-241, 268-297, 335-354 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_polar.py 16 11 31% 98-112 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_decomp_qz.py 127 110 13% 19-34, 38-43, 47-52, 56-61, 65-71, 76-145, 262-265, 358-405 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_expm_frechet.py 153 138 10% 91-114, 122-127, 166-173, 177-187, 191-203, 207-222, 226-278, 298, 334-350, 393-411 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_matfuncs_sqrtm.py 85 74 13% 52-116, 163-196 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_procrustes.py 18 13 28% 76-91 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_sketches.py 15 8 47% 49-54, 167-168 /usr/local/lib/python3.8/dist-packages/scipy/linalg/_solvers.py 216 194 10% 86-107, 159-199, 214-218, 228-233, 306-324, 446-528, 652-736, 778-844 /usr/local/lib/python3.8/dist-packages/scipy/linalg/basic.py 385 346 10% 27-37, 137-258, 330-359, 433-472, 568-596, 669-702, 706-711, 862-907, 952, 979, 981, 1034-1043, 1157-1246, 1304-1318, 1373-1391, 1451-1470, 1575-1619 /usr/local/lib/python3.8/dist-packages/scipy/linalg/blas.py 86 12 86% 301-310, 341, 352, 381-384 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp.py 364 338 7% 41-47, 51-73, 78-115, 214-267, 374-489, 503-530, 640-695, 767, 858, 951, 1031, 1124-1194, 1199-1203, 1252-1283, 1368-1431 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_cholesky.py 72 61 15% 19-44, 90-92, 154-156, 194-213, 274-286, 334-353 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_lu.py 48 38 21% 71-86, 135-148, 209-223 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_qr.py 130 121 7% 16-25, 121-173, 251-320, 386-424 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_schur.py 104 85 18% 119-178, 191-197, 201-210, 266-295 /usr/local/lib/python3.8/dist-packages/scipy/linalg/decomp_svd.py 88 60 32% 111, 116, 118, 132, 134, 139, 225-232, 273-281, 323-330, 384-391, 459-496 /usr/local/lib/python3.8/dist-packages/scipy/linalg/flinalg.py 30 23 23% 14-19, 23, 32-58 /usr/local/lib/python3.8/dist-packages/scipy/linalg/lapack.py 45 4 91% 808, 814, 826, 830 /usr/local/lib/python3.8/dist-packages/scipy/linalg/linalg_version.py 5 0 100% /usr/local/lib/python3.8/dist-packages/scipy/linalg/matfuncs.py 130 103 21% 52-55, 84-89, 136-138, 195-208, 255-256, 291-295, 330-334, 371-372, 409-410, 447-448, 485-486, 551-590, 626-670 /usr/local/lib/python3.8/dist-packages/scipy/linalg/misc.py 42 30 29% 141-181, 192-194 /usr/local/lib/python3.8/dist-packages/scipy/linalg/special_matrices.py 223 197 12% 62-73, 104-106, 138-140, 193-203, 239-244, 290-301, 344-358, 413-430, 465-471, 535-553, 600-617, 654-662, 698-700, 759-777, 840-863, 938-973, 1033-1043, 1109-1119, 1172-1196 /usr/local/lib/python3.8/dist-packages/scipy/misc/__init__.py 10 0 100% /usr/local/lib/python3.8/dist-packages/scipy/misc/common.py 73 65 11% 35-47, 85-120, 154-159, 195-204, 297-303 /usr/local/lib/python3.8/dist-packages/scipy/misc/doccer.py 29 8 72% 15, 21, 27, 33, 39, 44, 49, 54 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_docstrings.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/ndimage/_ni_support.py 43 13 70% 42, 44, 46, 48, 52, 64-65, 78, 80-81, 83, 89, 91 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/filters.py 399 298 25% 81, 85, 90, 129-133, 141, 156-164, 332-340, 369-377, 404-419, 447-449, 487-495, 525-544, 583-591, 598-623, 648, 754, 783-795, 842-857, 897-909, 954-966, 971-1033, 1069, 1106, 1113-1162, 1201-1202, 1240, 1280, 1348-1364, 1421-1448 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/fourier.py 69 58 16% 42-55, 59-70, 120-129, 179-187, 241-249, 298-306 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/interpolation.py 210 182 13% 93-105, 127-139, 245-263, 329-351, 433-487, 520-539, 589-616, 677-746 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/measurements.py 315 288 9% 178-236, 298-305, 377-458, 463-466, 500-573, 617-618, 669-670, 721-722, 773, 782-884, 946, 1025, 1086, 1155-1164, 1210-1219, 1275-1293, 1355-1364, 1419-1424, 1459-1498 /usr/local/lib/python3.8/dist-packages/scipy/ndimage/morphology.py 403 368 9% 50-53, 106-122, 208, 210, 218-285, 381, 501-513, 629-636, 775-782, 871-892, 1022, 1097-1107, 1213-1216, 1338-1364, 1443-1447, 1526-1530, 1636-1643, 1681-1695, 1739-1752, 1797-1810, 1872-1947, 1986-2063, 2175-2230 /usr/local/lib/python3.8/dist-packages/scipy/optimize/__init__.py 26 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_basinhopping.py 219 182 17% 20, 23-24, 27-31, 34, 61-92, 102-146, 151-173, 177-178, 208-216, 219, 222-232, 238-242, 246-247, 264-265, 268-270, 278-280, 283-286, 304-305, 313-315, 321, 626-701, 705-707, 711-716, 720-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_constraints.py 180 157 13% 94-101, 136-139, 168-170, 173-176, 215-251, 267-274, 284-294, 304-307, 312-317, 324-411, 419-450 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentiable_functions.py 351 317 10% 31-160, 163-165, 168-170, 173-175, 178-181, 184-187, 190-193, 196-200, 223-420, 423-425, 428-429, 432-434, 437-439, 442-444, 447-449, 452-454, 458-461, 472-489, 492-494, 497-501, 504-505, 508-510, 521-528 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_differentialevolution.py 364 310 15% 294-308, 475-599, 607-637, 644-652, 666-687, 694, 702-704, 711, 730-849, 869-889, 894-908, 928, 948-959, 962, 965, 969-970, 974-975, 1011-1020, 1035-1144, 1150, 1154, 1158-1159, 1163-1195, 1199-1200, 1205-1206, 1211-1216, 1220-1224, 1228-1233, 1237-1242, 1249-1253, 1261-1262, 1265, 1294-1322, 1325, 1341-1346 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_dual_annealing.py 285 250 12% 55-71, 79-111, 115-127, 153-159, 166-195, 199-205, 209-210, 240-256, 259-277, 280-306, 315-355, 362-370, 373-374, 388-405, 409-425, 602-689 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_hessian_update_strategy.py 134 94 30% 52, 70, 87, 100, 136-145, 151-159, 162, 180-201, 217-220, 231-237, 279, 282-288, 311-312, 329-330, 334-375, 407-408, 412-430 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog.py 78 65 17% 78-110, 155-161, 510-581 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_ip.py 247 217 12% 84-119, 189-329, 345-349, 368-375, 399-412, 427-432, 447-453, 471-502, 534-544, 698-822, 1083-1127 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_rs.py 190 171 10% 47-99, 108-134, 161-237, 249-269, 277, 285-288, 295-310, 327-402, 522-558 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_simplex.py 107 98 8% 89-95, 154-166, 212-229, 355-435, 591-659 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_linprog_util.py 489 470 4% 57-68, 92-99, 118-121, 183-386, 497-778, 873-881, 971-1085, 1092, 1100-1130, 1138-1145, 1170-1173, 1241-1293, 1350-1396, 1473-1485 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsap.py 20 17 15% 79-105 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/bvls.py 116 109 6% 13-16, 20-177 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/common.py 295 261 12% 36-56, 108-170, 196-221, 235-247, 284-301, 316-324, 350-363, 371, 392-400, 418-439, 448-466, 499-510, 515-541, 548, 555-565, 571, 578-588, 597-600, 605-615, 620-631, 637-648, 660-671, 679-689, 697-707, 712-722, 730-736 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/dogbox.py 149 138 7% 65-77, 94-106, 125-149, 154-330 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/least_squares.py 255 229 10% 43-92, 98-105, 109-126, 130-149, 153-162, 169-177, 181-186, 190-195, 199-204, 212-237, 748-940 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/lsq_linear.py 82 70 15% 16-24, 218-317 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf.py 290 278 4% 121-126, 133-205, 210-402, 410-564 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_lsq/trf_linear.py 144 132 8% 53-69, 74-90, 95-142, 147-248 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_minimize.py 176 155 12% 479-636, 756-794, 799-806, 811-829 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_numdiff.py 254 237 7% 46-91, 100-103, 107-114, 147-175, 330-398, 404-441, 445-481, 486-561, 625-639 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_remove_redundancy.py 147 136 7% 30-31, 53-54, 83-92, 96-104, 139-230, 266-357, 393-449 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root.py 89 69 22% 153-203, 207-208, 246-257, 266-305, 369, 434, 476, 513, 550, 590, 654 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_root_scalar.py 124 104 16% 30-33, 38-43, 47-49, 53-55, 58, 181-287, 306, 325, 343, 366, 392, 423, 442, 461 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo.py 657 595 9% 417-447, 456-655, 673-707, 721-737, 744-757, 762-778, 782-789, 793-795, 799-800, 803-804, 814-835, 838-845, 855-868, 871-879, 888-899, 907-910, 917-955, 974-1020, 1024-1028, 1031-1034, 1044-1052, 1070-1086, 1102-1104, 1124-1182, 1190-1199, 1203-1206, 1225-1240, 1245-1282, 1290-1292, 1302-1357, 1366-1375, 1384-1390, 1395-1403, 1407-1409, 1415-1421, 1431-1458, 1466-1472, 1476-1508, 1514-1531, 1534-1542, 1550, 1555-1568, 1574-1596, 1601-1605, 1610-1617, 1620-1628, 1631-1645, 1651-1671 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/sobol_seq.py 122 114 7% 30-40, 53-58, 97-102, 140-145, 197-372 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_shgo_lib/triangulation.py 359 321 11% 8-46, 49, 56-85, 89-115, 119-141, 146-159, 163-174, 182, 195-225, 231-243, 258-298, 315-362, 372-451, 456-464, 467, 470-471, 477-480, 487-490, 497, 503-504, 513-517, 528-530, 536-569, 572, 575-585, 588-592, 596-600, 603-609, 616-626, 629-661 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_spectral.py 106 93 12% 66-164, 207-238, 245-251, 255, 259 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trlib/__init__.py 6 3 50% 7-12 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion.py 135 114 16% 25-35, 38, 43-45, 50-52, 57-59, 62-65, 70-72, 80-95, 98, 133-266 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/canonical_constraint.py 253 234 8% 43-48, 53-69, 78-91, 101-149, 153-181, 185-221, 225-261, 265-327, 337-390 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py 105 96 9% 14-15, 50-218 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py 175 152 13% 29-30, 33-36, 46-48, 51-57, 62-100, 107-112, 314-544 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/projections.py 164 145 12% 10, 41-55, 62-90, 96-172, 179-233, 240-287, 364-406 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/qp_subproblem.py 215 201 7% 45-63, 99-149, 189-234, 286-303, 308, 313, 364-409, 492-638 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/report.py 32 10 69% 11-16, 23-28, 32 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_constr/tr_interior_point.py 148 124 16% 38-57, 60-61, 64, 67, 80-86, 93-96, 100, 108-114, 130-136, 140, 143-163, 179-195, 199-205, 209-221, 226-240, 251-264, 287-347 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_dogleg.py 40 30 25% 31-35, 47-51, 57-62, 98-124 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_exact.py 139 124 11% 35-41, 80-122, 137-143, 173-185, 218-254, 264-285, 290-432 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_krylov.py 11 7 36% 22-59 /usr/local/lib/python3.8/dist-packages/scipy/optimize/_trustregion_ncg.py 51 42 18% 33-39, 71-128 /usr/local/lib/python3.8/dist-packages/scipy/optimize/cobyla.py 72 60 17% 139-170, 196-258 /usr/local/lib/python3.8/dist-packages/scipy/optimize/lbfgsb.py 136 123 10% 174-208, 265-380, 412-422, 440-456, 468-478 /usr/local/lib/python3.8/dist-packages/scipy/optimize/linesearch.py 320 299 7% 69-103, 150-186, 267-322, 384-468, 481-502, 512-523, 532-603, 644-659, 666-668, 685-726, 773-802, 852-883 /usr/local/lib/python3.8/dist-packages/scipy/optimize/minpack.py 284 255 10% 26-45, 139-165, 206-259, 384-457, 461-478, 482-491, 495-508, 683-813, 821-844, 848, 852, 856-870, 914-916 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nnls.py 21 16 24% 60-82 /usr/local/lib/python3.8/dist-packages/scipy/optimize/nonlin.py 628 491 22% 139, 144-147, 152-154, 158-160, 269-367, 375-415, 434-453, 456-472, 521-530, 533, 536, 539, 542-547, 552-558, 562, 566, 573-658, 667-678, 681, 684-688, 703-708, 712-718, 723-746, 750-752, 756-758, 762-764, 768-770, 773-781, 784-790, 794-797, 803-808, 814-819, 850-884, 951-973, 977-978, 981, 984-988, 991, 994, 997, 1000-1006, 1046-1051, 1115-1121, 1124-1144, 1147-1167, 1170-1192, 1225-1226, 1229-1230, 1233, 1236, 1239, 1242, 1245, 1248, 1275-1276, 1279, 1282, 1285, 1288, 1291, 1294, 1325-1328, 1331-1332, 1335, 1338, 1341, 1344, 1347, 1350-1353, 1438-1476, 1479-1481, 1484-1491, 1494-1498, 1501-1508, 1511-1524 /usr/local/lib/python3.8/dist-packages/scipy/optimize/optimize.py 1239 1166 6% 59-61, 64-67, 70-74, 115-118, 124-129, 132, 140-145, 152, 159-164, 197-200, 229-238, 270-277, 310-317, 321-329, 435-453, 498-682, 690-707, 765, 809-813, 818-820, 841-869, 945-964, 990-1098, 1253-1271, 1297-1406, 1500-1519, 1545-1672, 1742-1750, 1771-1894, 1901-1910, 1914, 1918-1945, 1949-2053, 2056-2059, 2132-2138, 2158-2171, 2234-2239, 2253-2315, 2352-2417, 2426-2430, 2550-2570, 2599-2712, 2716-2728, 2903-2985, 2993-2994, 2998, 3069-3166, 3170-3242, 3246 /usr/local/lib/python3.8/dist-packages/scipy/optimize/slsqp.py 190 178 6% 57-66, 181-212, 237-475, 483-531 /usr/local/lib/python3.8/dist-packages/scipy/optimize/tnc.py 102 76 25% 243-277, 342-413, 420-441 /usr/local/lib/python3.8/dist-packages/scipy/optimize/zeros.py 471 417 11% 54-62, 65-68, 73-81, 86-93, 266-365, 377-469, 546-554, 643-651, 773-781, 878-886, 896-899, 904-908, 919-927, 932-937, 949-972, 980-992, 1002, 1013-1037, 1048-1064, 1067-1077, 1081-1085, 1089, 1092, 1096-1123, 1127-1132, 1139-1213, 1218-1244, 1353-1375 /usr/local/lib/python3.8/dist-packages/scipy/signal/__init__.py 44 2 95% 330, 343 /usr/local/lib/python3.8/dist-packages/scipy/signal/_arraytools.py 49 41 16% 43-46, 54, 92-106, 143-155, 196-209, 237-243 /usr/local/lib/python3.8/dist-packages/scipy/signal/_max_len_seq.py 31 25 19% 104-137 /usr/local/lib/python3.8/dist-packages/scipy/signal/_peak_finding.py 225 201 11% 66-81, 138, 194, 248-250, 264-267, 281-293, 307-319, 459-462, 584-590, 625-640, 673-678, 713-723, 935-1006, 1055-1126, 1164-1190, 1283-1299 /usr/local/lib/python3.8/dist-packages/scipy/signal/_savitzky_golay.py 81 70 14% 98-141, 153-165, 179-209, 219-223, 328-353 /usr/local/lib/python3.8/dist-packages/scipy/signal/_upfirdn.py 40 30 25% 59-63, 67-69, 75-86, 90-100, 207-210 /usr/local/lib/python3.8/dist-packages/scipy/signal/bsplines.py 202 177 12% 19, 28-43, 60-114, 125-129, 148-149, 157-167, 175-185, 189-193, 197, 202-206, 210-237, 241-252, 256-267, 292-295, 319-322, 337-358, 373-394 /usr/local/lib/python3.8/dist-packages/scipy/signal/filter_design.py 1094 1014 7% 47-56, 94-117, 180-193, 255-272, 424-477, 562-583, 662-693, 698-706, 821-828, 881-932, 987-1000, 1064-1071, 1095-1130, 1168, 1193-1200, 1230-1240, 1245-1250, 1421-1515, 1539-1561, 1592-1632, 1691-1704, 1765-1787, 1851-1876, 1938-1965, 2017-2046, 2155-2175, 2294-2386, 2393-2398, 2457-2474, 2520-2534, 2583-2600, 2652-2679, 2731-2759, 2866, 2983, 3094, 3218, 3380, 3385, 3389, 3421-3445, 3525-3615, 3693-3753, 3833-3915, 3993-4054, 4067-4074, 4091-4112, 4129-4154, 4161-4163, 4167-4179, 4201-4263, 4285-4288, 4318-4332, 4340-4357, 4371-4396, 4404-4435, 4449-4463, 4540-4568, 4648, 4728, 4764-4803 /usr/local/lib/python3.8/dist-packages/scipy/signal/fir_filter_design.py 272 250 8% 26-32, 79-85, 127-128, 250-261, 389-482, 593-684, 836-855, 968-1068, 1082-1090, 1215-1265 /usr/local/lib/python3.8/dist-packages/scipy/signal/lti_conversion.py 160 142 11% 76-114, 118-121, 125-126, 130-133, 137-139, 143-148, 176-195, 256-284, 304, 334, 405-504 /usr/local/lib/python3.8/dist-packages/scipy/signal/ltisys.py 919 757 18% 54-58, 66-70, 75, 79-82, 87, 92, 103-106, 117-120, 131-134, 206-221, 229, 236, 243, 250, 274, 284, 295, 383-398, 406-409, 414, 418, 425, 432, 439, 468, 479, 561-578, 583-592, 596, 606, 610-617, 622, 626, 638-639, 651, 663, 676, 697-702, 722-727, 800, 940-958, 963-972, 976, 987, 991-998, 1003, 1007, 1012, 1016, 1028-1030, 1042, 1055, 1067, 1131, 1207-1208, 1304-1317, 1322-1333, 1337, 1347, 1365-1404, 1411-1421, 1428, 1434-1473, 1479-1482, 1485-1488, 1491-1494, 1501-1508, 1513, 1517, 1522, 1526-1527, 1532, 1536-1537, 1542, 1546, 1558-1561, 1578, 1596, 1609, 1677, 1799-1853, 1861-1867, 1926-2032, 2058-2064, 2114-2133, 2200-2222, 2277-2292, 2358-2373, 2432-2437, 2494-2522, 2529, 2540-2580, 2590-2600, 2613-2636, 2655-2710, 2720-2763, 2777-2886, 2896-2912, 3095-3263, 3322-3379, 3431-3465, 3516-3550, 3615-3648, 3712-3722 /usr/local/lib/python3.8/dist-packages/scipy/signal/signaltools.py 1148 1072 7% 27, 47-50, 55-58, 77-93, 197-259, 265-270, 305-331, 365-391, 419-428, 523-544, 572-655, 736-860, 876-881, 889-892, 904-935, 963-977, 984-985, 996-1002, 1019-1037, 1146-1174, 1267-1296, 1353-1359, 1394-1409, 1437-1460, 1537-1549, 1627-1644, 1681-1692, 1817-1885, 1928-1953, 1997-2009, 2098-2120, 2145-2180, 2208-2210, 2265-2297, 2356-2372, 2377-2397, 2401-2424, 2503-2539, 2598-2644, 2648-2675, 2733-2749, 2832-2924, 3054-3134, 3184-3212, 3254-3297, 3391-3433, 3489-3505, 3557-3684, 3844-3885, 3890-3920, 3924-3927, 4000-4039, 4130-4151, 4202-4245 /usr/local/lib/python3.8/dist-packages/scipy/signal/spectral.py 363 339 7% 142-158, 268-289, 452-457, 584-601, 734-771, 870-895, 996-1022, 1172-1178, 1348-1456, 1566-1576, 1669-1870, 1896-1920, 1959-1981, 2001-2002 /usr/local/lib/python3.8/dist-packages/scipy/signal/waveforms.py 120 107 11% 58-88, 139-162, 224-262, 427-430, 440-483, 577-580, 591-593, 669-681 /usr/local/lib/python3.8/dist-packages/scipy/signal/wavelets.py 136 123 10% 29-76, 90-92, 127-198, 253-261, 301-308, 384-388, 462-473 /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/signal/windows/windows.py 289 243 16% 21-23, 28-31, 36-39, 112-121, 168-174, 226-238, 290-302, 349-357, 442, 501, 548, 609-610, 700-708, 790, 795, 856-878, 925-933, 1020, 1098, 1207-1216, 1271-1279, 1342-1349, 1438-1476, 1545-1563, 1616-1622, 1698-1710, 1876-1970, 1975-1982, 2095-2124 /usr/local/lib/python3.8/dist-packages/scipy/sparse/__init__.py 19 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/_index.py 221 190 14% 24-27, 35-75, 78-126, 129-150, 157-180, 185-191, 196-202, 205, 208, 211, 214, 217, 220, 223, 226, 229, 232, 235, 238, 242-244, 252-283, 288-322, 326-328 /usr/local/lib/python3.8/dist-packages/scipy/sparse/_matrix_io.py 42 32 24% 16, 63-80, 131-156 /usr/local/lib/python3.8/dist-packages/scipy/sparse/base.py 455 344 24% 71-75, 81-82, 86, 123-131, 157, 182-189, 194-203, 207-208, 212, 223, 239, 250, 254, 257-258, 263-281, 284-287, 295, 313-328, 340, 344, 348, 363, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 407, 410-424, 427, 430-443, 446-455, 466-530, 534, 537, 540, 543, 546-554, 561-564, 567-570, 577-617, 620, 624, 628, 632, 635, 638, 641, 644, 647, 650, 653-675, 678-691, 718, 736-741, 744, 756, 759, 762, 780-782, 791-799, 808-816, 851, 883, 894, 902, 910, 918, 926, 937, 945, 953, 993-1025, 1064-1097, 1124, 1146-1149, 1152-1176, 1179-1189 /usr/local/lib/python3.8/dist-packages/scipy/sparse/bsr.py 315 266 16% 123-214, 225-271, 279, 283-287, 292-293, 299-308, 317, 320, 330, 336, 339, 342-351, 354-364, 367-421, 436-441, 444-463, 468, 479-505, 508, 513-534, 546-561, 568-594, 599-607, 613-627, 635-676, 684-688, 722 /usr/local/lib/python3.8/dist-packages/scipy/sparse/compressed.py 737 654 11% 31-108, 111-123, 130-136, 148-195, 212-215, 219-248, 252-279, 283-313, 316, 322, 328, 334, 344-352, 355, 358, 365-458, 465-475, 478-489, 492-528, 531-539, 548-568, 571, 577, 592-611, 630-635, 642-647, 650-653, 657-668, 672-673, 678-698, 704-730, 736-758, 764-775, 782-793, 797-798, 801-802, 806-823, 826-853, 856-872, 880-911, 918-931, 942-1003, 1010-1017, 1023-1037, 1050-1053, 1068-1074, 1077-1079, 1089-1098, 1110-1113, 1116, 1123-1125, 1135-1138, 1143-1153, 1156-1187, 1201-1207, 1212-1242, 1248-1269, 1273-1290 /usr/local/lib/python3.8/dist-packages/scipy/sparse/construct.py 232 206 11% 62, 138-188, 218, 252-273, 311-355, 384-398, 406-431, 465, 499, 545-623, 668-677, 750-793, 842 /usr/local/lib/python3.8/dist-packages/scipy/sparse/coo.py 294 251 15% 129-198, 201-236, 242-263, 271-291, 294-300, 306-317, 323-330, 352-372, 394-414, 417-420, 425-443, 448-454, 459-475, 480-513, 521-525, 533-537, 541-554, 561-564, 571-579, 583-586, 589-593, 619 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csc.py 87 58 33% 111-119, 125-126, 129-132, 137-155, 164-180, 188-194, 200-206, 209, 212-214, 217-219, 222, 225, 228, 235, 261 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_laplacian.py 51 44 14% 69-81, 85, 89-111, 115-128 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csgraph/_validation.py 31 25 19% 17-58 /usr/local/lib/python3.8/dist-packages/scipy/sparse/csr.py 135 103 24% 129-137, 143-156, 161-164, 169-186, 191-222, 231, 234-242, 248-256, 263-271, 275, 278-307, 311-313, 316, 319, 322-325, 351 /usr/local/lib/python3.8/dist-packages/scipy/sparse/data.py 184 138 25% 23, 26, 29, 33-35, 38, 41, 44, 47, 50-53, 56-60, 63-68, 71-79, 84-89, 94, 99, 113-119, 126, 135-136, 148-156, 166-187, 191-215, 218-252, 255-289, 321, 353, 376, 399 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dia.py 224 188 16% 79-146, 149-150, 158-164, 167-168, 171-181, 187-225, 230-241, 244, 247-276, 279-282, 287-305, 311-318, 323-343, 349-365, 374-377, 380-392, 420 /usr/local/lib/python3.8/dist-packages/scipy/sparse/dok.py 275 213 23% 23, 79-111, 115, 122, 125-128, 133-136, 139, 145, 151-158, 161, 164, 167, 170-191, 194, 197, 200-201, 204-205, 209-216, 220-227, 230-234, 237-246, 249-277, 280-301, 304-309, 312-316, 320-323, 327-332, 335-338, 341-346, 349-352, 358, 365-374, 380-384, 387-389, 394-404, 409-411, 416, 421-429, 457 /usr/local/lib/python3.8/dist-packages/scipy/sparse/extract.py 22 14 36% 38-42, 101-103, 162-164, 168-171 /usr/local/lib/python3.8/dist-packages/scipy/sparse/lil.py 291 232 20% 89-132, 135-136, 139-140, 143-147, 150-154, 160-172, 175, 181-185, 190-193, 198-206, 210-216, 220-226, 229-231, 234-235, 238, 241, 244-245, 248, 251-252, 255-256, 260-261, 265-271, 289-300, 303, 307-308, 314-324, 328-336, 339-350, 353-360, 363-369, 374-401, 406-426, 431-435, 440, 445-448, 454-484, 512-527, 553 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_expm_multiply.py 255 225 12% 18-23, 28-33, 38-43, 48-55, 140-144, 172-197, 204-223, 313, 342-346, 352, 358-360, 366-369, 375, 399, 414-417, 455-475, 506-511, 556-629, 639-648, 655-677, 684-713 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_norm.py 70 63 10% 15-19, 110-184 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/_onenormest.py 199 177 11% 86-119, 130-139, 154-157, 162, 166-174, 178-180, 187-190, 194-197, 204-211, 215, 219, 261-322, 366-468 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/_add_newdocs.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/dsolve/linsolve.py 197 173 12% 56-59, 63-82, 132-233, 302-324, 386-410, 442-469, 528-607 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/arpack/arpack.py 726 642 12% 280-281, 297-299, 307, 313-364, 367-377, 435-533, 536-573, 576-595, 636-719, 722-759, 762-896, 900-904, 914-917, 922-927, 937-939, 942, 949-951, 961-974, 977-982, 992-1020, 1023-1028, 1032, 1037-1044, 1048-1054, 1058-1089, 1251-1349, 1554-1689, 1694-1713, 1717, 1721, 1804-1910 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/eigen/lobpcg/lobpcg.py 323 310 4% 34-44, 52-57, 64-72, 77-79, 84-114, 119-125, 287-711 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/interface.py 346 245 29% 140-152, 160-168, 173-175, 184, 196, 222-243, 269-290, 294-298, 324-339, 364-377, 381-384, 387, 390, 407-419, 423-426, 429-432, 435-438, 441-444, 447-450, 453, 456, 459-465, 481, 491, 497, 501, 509-518, 521-524, 527, 530-533, 536-539, 542, 553-556, 559, 562, 565, 568, 573-576, 580, 583, 587, 590, 593-598, 603-610, 613, 616, 619, 622, 625-626, 631-639, 642, 645, 648, 651, 654-655, 660-666, 669, 672, 675, 678, 681-682, 687-695, 698-701, 704, 707, 710, 713, 716-717, 722-725, 728, 731-733, 737-740, 744, 747, 752, 755, 758, 761, 764, 767, 795-823 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/__init__.py 11 0 100% /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/_gcrotmk.py 192 182 5% 66-182, 267-490 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/iterative.py 421 386 8% 73-77, 97-118, 137-198, 209-265, 276-337, 347-414, 514-647, 717-802 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lgmres.py 69 59 14% 128-235 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsmr.py 185 177 4% 197-482 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/lsqr.py 200 192 4% 81-95, 311-570 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/minres.py 203 196 3% 71-343, 347-363 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/isolve/utils.py 56 46 18% 23-27, 31, 65-123 /usr/local/lib/python3.8/dist-packages/scipy/sparse/linalg/matfuncs.py 354 279 21% 77-82, 103-115, 120-130, 155-173, 179-188, 191-193, 196-200, 203-205, 209, 218-235, 238-240, 243-246, 249-251, 255-256, 296, 338, 367-383, 387-390, 394-397, 401-404, 408-411, 415-418, 422-424, 428-430, 434-436, 440-442, 446-454, 458-466, 470-478, 482-490, 493-498, 501-506, 509-514, 517-525, 528-547, 595, 603-677, 700-709, 736-740, 762-764, 783-813, 833-855 /usr/local/lib/python3.8/dist-packages/scipy/sparse/sputils.py 176 146 17% 42-53, 58-63, 70, 80-90, 94, 105-118, 143-171, 176-180, 185, 194-207, 215-225, 229, 235, 241, 245-264, 269-314, 326-331, 338-339, 346-349, 353-356, 360-363 /usr/local/lib/python3.8/dist-packages/scipy/spatial/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/_plotutils.py 78 65 17% 11-27, 31-35, 81-90, 136-148, 212-264 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_procrustes.py 25 20 20% 101-132 /usr/local/lib/python3.8/dist-packages/scipy/spatial/_spherical_voronoi.py 68 57 16% 133-166, 171-206, 217-243, 273-277 /usr/local/lib/python3.8/dist-packages/scipy/spatial/distance.py 633 503 21% 135-165, 170-172, 176-178, 182-183, 194-195, 200-220, 224-240, 244-259, 263-269, 273-286, 290-292, 307-309, 314-329, 334-339, 343-346, 350-357, 451-457, 508-524, 580-581, 620, 661-674, 708-721, 766, 813-820, 883-894, 941-950, 985-991, 1031-1037, 1079-1084, 1122-1130, 1170-1178, 1221-1236, 1286-1296, 1337-1342, 1353, 1397-1412, 1457-1462, 1507-1519, 1565-1578, 1624-1638, 1718, 1722, 1724-1731, 1999, 2006-2012, 2017, 2023, 2027, 2031, 2039-2050, 2069-2094, 2149-2212, 2254-2302, 2334-2339, 2344-2358, 2378-2380, 2403, 2407, 2708-2793 /usr/local/lib/python3.8/dist-packages/scipy/spatial/kdtree.py 419 379 10% 39-55, 78-83, 93-95, 98, 102, 120-126, 140, 154, 168, 182, 243-251, 256, 259, 262, 265, 268, 272-273, 277-281, 284-323, 329-407, 492-547, 550-572, 625-636, 663-705, 731-812, 842-889, 912-942, 978-996 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/__init__.py 7 0 100% /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_groups.py 56 48 14% 6-58, 62-76, 80-90, 94-99, 103-105, 109-140 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/_rotation_spline.py 176 159 10% 18-25, 30, 48-65, 83-104, 124-151, 168, 188, 220-248, 331-361, 364-404, 424-456 /usr/local/lib/python3.8/dist-packages/scipy/spatial/transform/rotation.py 470 416 11% 15-17, 29-142, 146-150, 154-158, 162-173, 369-394, 407, 474-479, 568-612, 618, 674-707, 803-860, 907-910, 964-999, 1004, 1052-1073, 1159-1179, 1298-1329, 1397-1406, 1439-1443, 1467-1475, 1510-1527, 1560-1617, 1655, 1703, 1722-1727, 1768-1775, 1783-1838, 1915-1968, 2049-2070, 2090-2114 /usr/local/lib/python3.8/dist-packages/scipy/special/__init__.py 17 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/_basic.py 524 449 14% 100-112, 179-213, 250-254, 279-285, 305, 325, 345, 365, 397-401, 433-437, 469-473, 481-487, 515-519, 547-551, 602-606, 635-639, 667-671, 699-703, 747-755, 799-807, 888, 926-928, 941-943, 956-958, 971-973, 988, 1032-1034, 1072-1093, 1129-1150, 1201-1226, 1284-1307, 1343-1360, 1373-1380, 1424-1431, 1449-1460, 1476-1487, 1535-1538, 1586-1589, 1624-1639, 1666-1676, 1703-1713, 1740-1749, 1762-1764, 1777-1779, 1792-1794, 1800-1802, 1815-1817, 1830-1832, 1845-1847, 1860-1862, 1878-1880, 1904-1911, 1928-1935, 1984-1996, 2035-2050, 2065-2070, 2120-2158, 2195-2215, 2260-2270, 2333-2336 /usr/local/lib/python3.8/dist-packages/scipy/special/_ellip_harm.py 16 6 62% 97, 155-156, 160, 208-209 /usr/local/lib/python3.8/dist-packages/scipy/special/_logsumexp.py 34 12 65% 96-99, 105-106, 109-110, 118-119, 123, 127 /usr/local/lib/python3.8/dist-packages/scipy/special/_spherical_bessel.py 18 12 33% 53-56, 104-107, 153-156, 202-205 /usr/local/lib/python3.8/dist-packages/scipy/special/lambertw.py 4 1 75% 107 /usr/local/lib/python3.8/dist-packages/scipy/special/orthogonal.py 525 464 12% 128-151, 154-157, 160-173, 191-216, 264-290, 331-346, 394-404, 442-457, 503-524, 569-586, 630, 662-676, 744-760, 789-796, 825-831, 860-871, 898-910, 947-1020, 1050-1067, 1113-1124, 1157-1172, 1229-1248, 1282-1297, 1345-1364, 1400-1408, 1455-1463, 1499-1512, 1557-1566, 1603-1608, 1652-1659, 1696-1714, 1758-1765, 1802-1821, 1865-1866, 1894-1902, 1945-1952, 1980-1985, 2029-2039, 2080-2094, 2137-2143, 2170-2182 /usr/local/lib/python3.8/dist-packages/scipy/special/sf_error.py 6 0 100% /usr/local/lib/python3.8/dist-packages/scipy/special/spfun_stats.py 14 8 43% 86-95 /usr/local/lib/python3.8/dist-packages/scipy/stats/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_binned_statistic.py 159 145 9% 167-182, 336-349, 514-634, 640-675, 681-706 /usr/local/lib/python3.8/dist-packages/scipy/stats/_constants.py 8 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_continuous_distns.py 2994 1970 34% 35-36, 50-54, 97, 100, 103, 106, 109, 152, 155, 158, 161, 164, 180, 184, 188, 192, 196, 200, 204, 208, 235, 239, 242, 245, 248, 251, 254, 257, 260, 263, 266, 273-300, 342, 345, 348, 351, 354, 382, 385, 388, 391, 394, 422, 425, 428, 431-435, 438, 449, 461-463, 470-472, 482-486, 514, 520, 523-525, 528, 531, 534-539, 542-553, 563-658, 690-693, 697, 700, 703, 706-724, 754, 757, 760, 763-775, 778-779, 834-840, 843-852, 855, 858, 861, 864, 867, 870-887, 890-894, 948, 951, 954, 957, 960, 963, 969, 972-973, 1011, 1014, 1017, 1021, 1024, 1027, 1030, 1033, 1036, 1039, 1068, 1071, 1074, 1077, 1080, 1083, 1086, 1090-1091, 1130-1131, 1137, 1140-1141, 1144, 1147, 1150-1155, 1187, 1191, 1194, 1197, 1200, 1203, 1206, 1209-1213, 1242, 1245, 1248, 1251, 1281-1284, 1288-1289, 1292-1293, 1296-1297, 1300-1301, 1304-1305, 1308-1309, 1338-1341, 1345-1347, 1350-1351, 1354-1355, 1358-1360, 1363, 1369, 1401, 1405, 1408, 1411, 1414, 1417, 1420, 1423, 1426, 1429, 1436-1473, 1518-1520, 1525-1530, 1533-1535, 1538-1540, 1543-1545, 1548-1552, 1602, 1605-1609, 1612-1613, 1616, 1652, 1655-1657, 1660, 1663, 1666, 1669, 1705-1709, 1714, 1717, 1721, 1724-1725, 1733-1739, 1766, 1771, 1774, 1777, 1808, 1814, 1817-1821, 1824, 1827, 1830, 1833-1860, 1897, 1900, 1904, 1907, 1913-1926, 1972, 1975, 1978, 1981, 1984, 1987, 1990, 1993, 2027, 2030, 2033, 2036, 2039, 2042, 2045-2050, 2053, 2089, 2093, 2097, 2101, 2105, 2109, 2113, 2117, 2121, 2125, 2129, 2133, 2137, 2141, 2145, 2149, 2153, 2157, 2161, 2165, 2169, 2173, 2177, 2209, 2213, 2217, 2221, 2225, 2229, 2233, 2237, 2241, 2245, 2249, 2253, 2257, 2261, 2265, 2269, 2273, 2277, 2281, 2285, 2289, 2293, 2297, 2328, 2331, 2334-2335, 2338-2339, 2342-2348, 2391, 2394-2399, 2403, 2406, 2411, 2414, 2417, 2422, 2425, 2428-2434, 2439, 2479, 2483, 2486, 2526, 2529-2531, 2534, 2541, 2544-2554, 2557, 2560, 2563, 2566-2567, 2571-2572, 2576-2605, 2609-2614, 2617-2621, 2624, 2643-2663, 2703, 2707, 2710, 2713, 2716, 2719, 2722, 2725, 2733-2734, 2742-2813, 2840-2848, 2854-2855, 2860, 2863, 2903, 2907, 2910, 2913-2916, 2919-2922, 2925-2927, 2930-2932, 2936, 2939-2940, 2969, 2972, 2977-2981, 2984-2987, 2990, 2993, 3023, 3026, 3029, 3032, 3035, 3069, 3072, 3075, 3078, 3081, 3084, 3088, 3122, 3125, 3128, 3131, 3134, 3137, 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5044-5045, 5051, 5092, 5095, 5098, 5101, 5104-5105, 5108-5112, 5208, 5211-5249, 5254, 5257-5289, 5295, 5298-5327, 5333-5352, 5358-5368, 5408, 5412, 5415, 5418, 5421-5422, 5471-5473, 5476, 5479, 5482, 5485, 5488-5492, 5495-5513, 5544, 5547, 5550, 5553-5558, 5592, 5595, 5598-5599, 5604-5605, 5608-5609, 5612-5613, 5616-5617, 5660, 5669-5676, 5681, 5684, 5687-5691, 5697-5707, 5739, 5742, 5748-5751, 5754-5757, 5760, 5763, 5766, 5769, 5772-5782, 5814, 5817-5820, 5823-5837, 5840, 5843, 5852-5876, 5906, 5909, 5912, 5915, 5918-5942, 5945, 5977, 5980, 5983, 5986, 5989, 5992, 5995-5996, 5999, 6053-6074, 6081, 6084-6090, 6097-6103, 6111-6116, 6119-6124, 6127-6139, 6142-6146, 6178, 6181, 6184, 6187, 6190, 6193, 6199, 6234, 6238, 6241, 6272, 6275, 6278, 6281, 6323, 6326, 6329, 6332, 6335, 6338-6339, 6370, 6374, 6377, 6380, 6383, 6386, 6389, 6392, 6395-6396, 6402, 6458, 6461, 6465, 6468, 6471, 6474, 6477, 6480, 6517, 6521-6523, 6526, 6529, 6538, 6541-6544, 6579, 6582, 6585-6588, 6591, 6629, 6632, 6635, 6638, 6643-6645, 6648, 6651, 6684, 6687, 6690-6700, 6703, 6706-6710, 6713-6725, 6754, 6757-6759, 6768, 6778-6783, 6812, 6815, 6823-6832, 6835-6844, 6847, 6850, 6856, 6885, 6888, 6892, 6895, 6898, 6901, 6906-6913, 6916-6917, 6933, 6939-6946, 6950-6967, 6972-6973, 6978-6981, 6986-7004, 7010-7013, 7018-7037, 7042-7045, 7050-7063, 7069-7072, 7077-7114, 7139, 7142, 7145, 7148, 7151, 7154, 7157, 7160, 7163, 7166-7185, 7189-7213, 7244, 7247-7250, 7253, 7256, 7259, 7262-7264, 7272, 7293, 7296, 7299, 7302, 7305, 7308, 7383-7455, 7493, 7497, 7500, 7503, 7506, 7538, 7542, 7545, 7548, 7551, 7580, 7584, 7587-7604, 7607-7610, 7613, 7655, 7658, 7661-7663, 7666-7668, 7671, 7674, 7677-7678, 7681, 7728, 7731, 7734, 7737, 7740, 7743, 7746, 7799-7809, 7815-7824, 7830-7841, 7844-7861, 7867-7885, 7893, 7904, 7952-7953, 7959, 7965, 8053-8070, 8076, 8082, 8088, 8092-8093, 8097-8101, 8107-8109 /usr/local/lib/python3.8/dist-packages/scipy/stats/_discrete_distns.py 446 296 34% 43, 46, 49, 52-54, 58, 61-63, 66-67, 70-73, 76-84, 87-89, 119, 122, 126, 129, 134, 137, 140, 143, 146, 149, 193-194, 197, 200, 203-205, 208, 211-230, 263, 266, 270, 273-274, 277-278, 282-283, 286-289, 292-298, 331, 334, 337, 340, 343-344, 347, 350-351, 354-356, 359-364, 433, 436, 439-441, 444-449, 454, 459-471, 474-476, 483-489, 492-501, 504-513, 545, 548, 552, 555-567, 598, 601, 604-605, 609, 612-613, 616-617, 620-623, 626-631, 666, 669, 672-673, 676, 679-680, 683-686, 690-691, 694-698, 701-702, 731, 734, 739-740, 743-744, 747-751, 754-764, 794, 797, 801-802, 805-806, 809-812, 815-821, 825-836, 839, 871, 874, 878-879, 882, 915, 918-921, 924-929, 932-935, 938, 976-977, 981-985, 988-992, 995-999, 1040-1043, 1046, 1049, 1052, 1055, 1058, 1061, 1064-1077 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distn_infrastructure.py 1393 1036 26% 42, 361-363, 367-395, 402-406, 413-417, 424-432, 436, 440, 443, 446, 449, 452, 455, 458, 461-463, 466, 469, 472-474, 477, 480, 483, 486, 489, 492, 495, 498, 501, 508-512, 516, 541-545, 567-571, 575, 579, 607, 611, 614, 617-620, 640-649, 665, 668, 671, 679, 733-734, 742-751, 768, 771, 778, 785-788, 801-859, 871-874, 895, 898-899, 902-903, 912-914, 917-918, 921, 924-925, 928, 931, 961-995, 1024-1110, 1135-1146, 1165-1192, 1219, 1241-1245, 1267-1271, 1293-1295, 1321-1328, 1349-1351, 1362-1368, 1612, 1621, 1629, 1643-1651, 1654, 1657-1678, 1683, 1686-1687, 1692, 1695, 1698, 1702, 1705-1706, 1709, 1736-1752, 1778-1795, 1819-1837, 1861-1880, 1904-1922, 1949-1968, 1992-2014, 2038-2060, 2063, 2066-2072, 2082-2089, 2092-2102, 2109-2114, 2118-2121, 2132-2176, 2276-2314, 2336-2380, 2402-2412, 2415-2438, 2516-2540, 2546-2550, 2554-2609, 2663-2676, 2822, 2851, 2878, 2885, 2893, 2912-2921, 2924, 2927, 2930, 2933-2935, 2938-2939, 2968-2969, 2991-3006, 3028-3044, 3066-3084, 3106-3125, 3147-3164, 3189-3207, 3229-3248, 3270-3294, 3297-3301, 3356-3386, 3394-3428, 3449-3463, 3475-3517, 3532, 3535, 3539-3541, 3544-3546, 3551-3557, 3560, 3563-3564, 3593-3602 /usr/local/lib/python3.8/dist-packages/scipy/stats/_distr_params.py 2 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/_hypotests.py 43 36 16% 80-132 /usr/local/lib/python3.8/dist-packages/scipy/stats/_multivariate.py 972 690 29% 50-53, 81-88, 108, 156-173, 177-179, 203, 207, 210-213, 223, 227, 361, 373-426, 434-444, 469-471, 493-497, 519-523, 550-555, 587-594, 626-633, 657-661, 681-683, 733-741, 744-747, 750, 753, 756-759, 762, 774-776, 938, 948-1005, 1013-1019, 1050-1055, 1078-1085, 1107, 1131-1142, 1175-1179, 1182-1186, 1189, 1192, 1227-1233, 1237-1277, 1299, 1412, 1429-1430, 1448-1452, 1470-1474, 1490-1493, 1510-1514, 1531-1539, 1559-1561, 1569-1570, 1573, 1576, 1579, 1582, 1585, 1588, 1739, 1742-1768, 1775-1805, 1808-1819, 1851-1864, 1887-1894, 1917, 1933, 1948-1950, 1966-1970, 1988-1990, 2006-2010, 2025-2027, 2051-2072, 2098-2118, 2142-2150, 2169, 2197-2199, 2223-2225, 2250-2253, 2256-2260, 2263, 2266-2267, 2270-2271, 2274-2275, 2278-2281, 2284, 2325-2357, 2471, 2495-2515, 2538-2542, 2566, 2582-2586, 2605-2607, 2623, 2639-2641, 2657-2664, 2682-2684, 2708-2733, 2757-2769, 2773, 2798-2812, 2815-2818, 2821, 2824-2825, 2828-2829, 2832-2833, 2836-2841, 2845, 3002, 3011-3023, 3032-3047, 3050-3058, 3061, 3082-3094, 3115, 3130-3132, 3147-3156, 3182-3196, 3218-3220, 3244-3251, 3254, 3257, 3260, 3263, 3266, 3269, 3348, 3355-3359, 3378-3401, 3430-3431, 3434, 3495-3499, 3518-3538, 3601-3615, 3631-3648, 3657-3689, 3718-3730, 3787-3791, 3810-3824 /usr/local/lib/python3.8/dist-packages/scipy/stats/_rvs_sampling.py 33 28 15% 123-169 /usr/local/lib/python3.8/dist-packages/scipy/stats/_stats_mstats_common.py 107 98 8% 93-144, 232-266, 271-285, 374-404 /usr/local/lib/python3.8/dist-packages/scipy/stats/_tukeylambda_stats.py 54 40 26% 68-102, 164-201 /usr/local/lib/python3.8/dist-packages/scipy/stats/contingency.py 36 28 22% 57-62, 99-109, 243-275 /usr/local/lib/python3.8/dist-packages/scipy/stats/distributions.py 9 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/kde.py 183 151 17% 194-209, 231-265, 293-320, 344-355, 375-388, 411-438, 465-475, 485, 495, 548-565, 571-581, 593, 600-632, 636-640, 644-648 /usr/local/lib/python3.8/dist-packages/scipy/stats/morestats.py 829 760 8% 127-136, 194-211, 278-306, 349-358, 416-421, 440-452, 457-471, 585-627, 705-718, 797-812, 894-910, 916-945, 1034-1059, 1129-1165, 1173-1202, 1270, 1345-1363, 1370-1385, 1470-1481, 1533-1536, 1606, 1661-1678, 1771-1825, 1853-1869, 1897-1906, 2014-2070, 2113-2166, 2225-2247, 2307-2371, 2421-2465, 2472-2475, 2548-2600, 2670-2725, 2847-2938, 3080-3162, 3167-3186, 3225-3255, 3295-3307, 3349-3361 /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats.py 4 0 100% /usr/local/lib/python3.8/dist-packages/scipy/stats/mstats_basic.py 947 845 11% 61-67, 71-79, 83-89, 116-128, 151-160, 196-211, 235-259, 299-324, 328-330, 335, 373-399, 459-528, 569-663, 676-727, 759-782, 798-828, 870-884, 924-938, 942-949, 982-996, 1037-1066, 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96-119, 153-172, 208-241, 283-289, 378-434 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw.py 112 92 18% 35-43, 114-143, 177-181, 225-226, 277-305, 345-369, 411, 453, 500, 563, 624, 695, 752, 840-848, 912-921, 936-949 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw3d.py 35 31 11% 33-63, 88-114 /usr/local/lib/python3.8/dist-packages/skimage/draw/draw_nd.py 19 16 16% 45-51, 97-108 /usr/local/lib/python3.8/dist-packages/skimage/exposure/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/skimage/exposure/_adapthist.py 106 95 10% 78-98, 126-233, 256-287, 312-317 /usr/local/lib/python3.8/dist-packages/skimage/exposure/exposure.py 138 117 15% 23-35, 61-74, 122-144, 181-184, 217-223, 254-266, 292-307, 405-428, 433-434, 485-494, 528-537, 577-586, 629-640 /usr/local/lib/python3.8/dist-packages/skimage/exposure/histogram_matching.py 20 17 15% 9-19, 53-70 /usr/local/lib/python3.8/dist-packages/skimage/feature/__init__.py 25 4 84% 35-36, 46-47 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56% 20-23, 35 /usr/local/lib/python3.8/dist-packages/skimage/io/_io.py 44 22 50% 45, 51, 55-56, 59-61, 92, 126-136, 157-159, 179, 201 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/imageio_plugin.py 7 1 86% 10 /usr/local/lib/python3.8/dist-packages/skimage/io/_plugins/matplotlib_plugin.py 86 67 22% 44-58, 70-78, 97-112, 148-165, 176-200, 207-208 /usr/local/lib/python3.8/dist-packages/skimage/io/collection.py 152 119 22% 47-52, 77-78, 85-95, 175-205, 209, 213, 216-239, 258-313, 317-323, 327-328, 332, 335, 347, 366, 390, 435-443, 448 /usr/local/lib/python3.8/dist-packages/skimage/io/manage_plugins.py 136 22 84% 78, 115-116, 123, 188, 192-196, 202-206, 257, 293, 304-306, 330-331, 344-347 /usr/local/lib/python3.8/dist-packages/skimage/io/sift.py 27 20 26% 41-69, 73, 77 /usr/local/lib/python3.8/dist-packages/skimage/io/util.py 27 13 52% 24-41 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464 15% 193-202, 217-227, 254-310, 332-347, 358-377, 382-384, 408, 430, 442-448, 460-466, 494-495, 512-513, 520-522, 525, 528, 532-536, 539, 551-555, 558-559, 563-579, 583, 600-615, 634-659, 662-665, 689-695, 703-709, 712-721, 737-787, 791, 805-825, 843-879, 893-895, 899, 917-927, 955-1060, 1073-1150, 1154-1159, 1165-1171, 1185-1247, 1272-1282, 1286-1294, 1318-1326 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/optimize/calibrator.py 32 20 38% 47-55, 75-78, 102-105, 118-121 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/util.py 175 140 20% 68-71, 83-92, 110-129, 145-160, 172-176, 196-218, 223-229, 252-269, 284-288, 302-327, 341-356, 370-387, 413-507 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/python/wrap_toco.py 10 2 80% 32, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/model_flags_pb2.py 45 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/toco_flags_pb2.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/lite/toco/types_pb2.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/__init__.py 116 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/__init__.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/arg_defaults.py 30 7 77% 66-70, 81, 87-88 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/asserts.py 18 8 56% 31-47, 51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/break_statements.py 68 47 31% 30-31, 34, 41-48, 52-63, 66-71, 74-105, 108-149 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46, 53-57, 60-65, 90-107, 110-123, 130-148, 151-180, 190-199, 202, 208-211, 214-217, 220-223, 226-229, 232-234, 238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/logical_expressions.py 66 23 65% 56-59, 72-77, 90-112, 119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/return_statements.py 187 49 74% 87, 99-102, 105-109, 122-126, 130-131, 144, 147, 166, 177, 258-266, 282-293, 296-315, 323-327, 330-331, 376 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/converters/slices.py 35 22 37% 37-45, 49-56, 59-80, 85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/ag_ctx.py 37 3 92% 58, 70, 73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/core/config_lib.py 28 3 89% 48, 60, 64 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785-788, 831-836 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/conversion.py 322 94 71% 87, 117, 134, 151, 210, 308-310, 345, 350, 355, 388, 397-398, 406-410, 418-419, 440, 443-444, 451-453, 459-465, 474-476, 482-484, 509, 514-521, 526, 528-529, 538-632, 638-639, 683-690, 700, 704, 715, 724, 751, 758-759 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/directives.py 16 7 56% 44-46, 95-98 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/lang/special_functions.py 33 20 39% 33-45, 53, 83-88, 113-119 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/__init__.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/control_flow.py 449 379 16% 109-119, 128-135, 142-191, 204-234, 241-263, 272-297, 341-372, 377-401, 407-439, 452-485, 497-526, 538-586, 606, 621-677, 684-704, 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428-436, 440, 444-446, 454-462, 466, 470-472, 477-497, 501-507, 514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/slices.py 55 31 44% 55-67, 72, 77-81, 86, 91-92, 97, 117-125, 130, 135, 140, 145-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/operators/special_values.py 27 10 63% 52, 55, 58-63, 66, 81, 93, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/anno.py 59 7 88% 40, 107, 134-138 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/ast_util.py 214 117 45% 101-105, 108-109, 112-113, 130-132, 137-142, 149-151, 154-157, 160-161, 164-170, 173-206, 222-227, 262-275, 294, 299, 309, 315-316, 318-319, 325, 330, 341, 350-351, 354-359, 362-378, 381-388, 392-394 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/cfg.py 416 121 71% 86-93, 132, 136-143, 188, 200, 334-335, 340, 356, 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258, 279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/pretty_printer.py 87 69 21% 30-33, 36-38, 41, 44, 47, 50, 53, 56-57, 62-125, 129-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/qual_names.py 141 45 68% 48, 51, 58, 61, 70, 77, 80, 88, 96, 104, 107, 120-122, 134-138, 153-161, 173, 180, 187-191, 198, 203, 210-215, 244, 252, 265-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/activity.py 341 103 70% 121-124, 134-135, 189, 204-205, 244-249, 261-264, 269-274, 279, 281, 290-300, 324, 327, 330-332, 347, 352-361, 364, 372-382, 387, 400-404, 407, 445-456, 461-463, 466, 469, 472, 475, 481-497, 554-568, 571-579, 586 /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/annos.py 17 1 94% 30 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/device_lib.py 15 8 47% 34-42 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/pywrap_tf_session.py 31 11 65% 53-59, 66-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py 620 490 21% 57, 62, 66, 70, 74, 78, 84, 141, 192, 201, 206-207, 231, 245, 261-278, 301-316, 319, 322-326, 349-362, 374-379, 382, 386-396, 408-413, 416, 419-422, 434-437, 440, 443-446, 476-498, 501-502, 515, 523, 544-570, 582, 597-600, 604, 608, 612, 616, 619, 650-704, 731-742, 753-755, 759-772, 777, 787, 791, 846, 952-967, 1014, 1038-1089, 1095-1184, 1222-1309, 1340-1361, 1364-1384, 1387-1388, 1396-1411, 1417-1437, 1441, 1446, 1454-1462, 1467-1479, 1485-1487, 1504-1505, 1586-1589, 1592-1601, 1604-1641, 1669-1675, 1738-1769, 1773-1784 /usr/local/lib/python3.8/dist-packages/tensorflow/python/client/timeline.py 288 227 21% 60-62, 81-88, 97-102, 112-118, 132-135, 148-150, 163-165, 180-183, 198-200, 215-217, 230-232, 244-246, 257-262, 285-292, 297, 302, 307, 312, 317, 322, 327, 335, 343, 362-372, 376-378, 382-384, 389-397, 401-414, 424-441, 453-455, 459-463, 467, 471-481, 485-512, 517-561, 569-608, 611-617, 633-636 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/compat.py 32 7 78% 49, 54, 119, 162-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compat/v2_compat.py 60 19 68% 92-113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/mlir/mlir.py 9 1 89% 41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/__init__.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/compiler/tensorrt/trt_convert.py 455 365 20% 58, 80-81, 86-88, 93-95, 105-108, 199-245, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/batching.py 107 64 40% 81-86, 133-136, 179-192, 243-255, 282-285, 293-310, 315, 323-361, 364, 368, 394-422, 426 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/cardinality.py 27 8 70% 66, 95-98, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/counter.py 23 6 74% 51-54, 60, 66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute.py 70 45 36% 48-67, 71, 75, 88-119, 123, 130-133, 137, 150-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/distribute_options.py 22 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/enumerate_ops.py 12 3 75% 55-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/error_ops.py 17 6 65% 50-53, 61-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/get_single_element.py 13 3 77% 62-66 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/grouping.py 177 125 29% 60-64, 106-124, 177-244, 252-268, 272-276, 283, 293-349, 353, 359, 362, 367, 375-388, 393-401, 407-413, 418-428, 433, 436, 439, 453-455, 459, 463, 467 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/interleave_ops.py 86 46 47% 95-101, 108-135, 138, 142, 169-226, 231, 272-276, 281, 290-291 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/iterator_ops.py 64 37 42% 32-38, 94-95, 184-221, 227-238, 243, 266-277, 281-284, 287, 290, 301-302, 313 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/optimization_options.py 76 10 87% 57, 63-66, 191, 209, 211, 232, 256, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/parsing_ops.py 61 41 33% 37-117, 121, 161-180 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_aggregator.py 23 6 74% 64-77, 128, 140 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_ops.py 32 14 56% 44-48, 67-71, 90-94, 101-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/stats_options.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/take_while_ops.py 25 11 56% 33-49, 52, 69-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/threading_options.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/unique.py 20 8 60% 45-48, 56-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/experimental/ops/writers.py 22 7 68% 77-79, 105-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/dataset_ops.py 1381 743 46% 203, 227-240, 252-279, 285, 289, 293, 306-318, 329, 354, 357, 367, 385, 406, 420, 423-427, 475-484, 640, 658-665, 668-673, 676, 734, 736, 744, 785-823, 840-846, 913, 958, 987, 1057-1084, 1105, 1137-1138, 1198, 1248, 1267, 1286, 1354, 1387, 1489-1497, 1620-1623, 1748-1751, 1780, 1804-1810, 1889-1891, 1919-1996, 2023-2024, 2051, 2064-2078, 2087, 2103, 2106-2150, 2186, 2189-2204, 2218, 2232, 2246, 2254, 2260, 2265, 2278, 2283, 2289, 2294, 2298, 2302, 2307, 2311, 2315, 2320, 2324, 2328, 2332, 2336, 2345, 2351-2355, 2393-2401, 2412, 2421, 2427, 2446, 2450, 2454, 2459, 2463, 2469, 2476-2477, 2480, 2483, 2486, 2489, 2492, 2496, 2499, 2505-2524, 2540-2545, 2574-2579, 2597-2600, 2619, 2639, 2659, 2750, 2754, 2756, 2758, 2761, 2771-2774, 2782, 2858-2873, 2877, 2885-2902, 2906, 2913-2914, 2917, 2921, 2927-2929, 2933, 2948, 2961, 2985, 2988, 2992, 2995, 2999-3002, 3013, 3016, 3021-3023, 3026-3028, 3031, 3034, 3037, 3088-3092, 3097, 3104, 3144, 3168, 3172-3173, 3181-3202, 3226, 3230-3231, 3244, 3250, 3256, 3325-3342, 3345, 3349, 3357-3390, 3393, 3397, 3405-3415, 3423-3430, 3434-3451, 3454, 3458, 3471-3474, 3477-3485, 3495-3501, 3506, 3514-3529, 3540-3543, 3546-3554, 3562-3566, 3571, 3601-3627, 3635-3641, 3649-3655, 3663-3672, 3680-3705, 3709, 3716, 3719, 3722, 3725, 3765-3774, 3794-3826, 3843-3849, 3855-3875, 3885-3952, 3956, 3974-3989, 3992, 3996, 3999, 4014-4051, 4054, 4058, 4061, 4073, 4085, 4100-4120, 4123, 4127, 4130, 4146-4209, 4213, 4217, 4220, 4228-4246, 4249, 4252, 4273, 4289-4313, 4317, 4324-4331, 4334, 4356, 4358, 4373-4383, 4391-4400, 4408-4415, 4437-4448, 4455-4459, 4463, 4471-4488, 4492, 4498-4534, 4545-4553, 4564-4572 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/iterator_ops.py 253 111 56% 70-72, 99-117, 189-220, 273-298, 311-316, 333-371, 418-428, 439-442, 457, 471, 485, 497, 501-504, 512-516, 540-541, 581, 591, 597, 628, 647-661, 671-672, 686, 700, 714, 740-741, 745-748, 762, 785, 797-806, 809-810, 828 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py 291 224 23% 43-140, 144, 148, 160-182, 186, 190, 196-208, 232-302, 306-320, 324-332, 335-340, 344-346, 350-365, 370, 381-385, 388-400, 412-414, 418, 421, 425-431, 435-437, 440, 450, 493-566, 574-582, 585, 588, 591-594, 597-602, 606, 610 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/optional_ops.py 77 26 66% 59, 75, 85, 98-103, 121, 132-133, 136, 141-143, 155, 159, 170, 174, 177, 181, 184, 188, 192, 195, 198, 201 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/ops/readers.py 198 115 42% 46-64, 80-90, 114-127, 131, 160-172, 176, 188-190, 196, 200, 216-229, 233, 242-291, 295, 299, 302, 336-349, 356, 362, 366, 378-380, 390, 398, 402, 430-447, 451, 488-505, 509, 524-527, 533, 537, 545-547 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/convert.py 23 13 43% 30-34, 49-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/nest.py 101 35 65% 47-50, 69-70, 75, 88-89, 91, 176, 180, 186, 225-244, 297, 302, 308, 314-320, 462 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/options.py 59 30 49% 23, 38-43, 46-49, 52-55, 81-84, 115, 120, 124, 131-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/random_seed.py 21 10 52% 42-58 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/structure.py 178 72 60% 45, 51, 57, 64, 100-114, 146-172, 198, 252, 350, 394-404, 435-439, 445-450, 456-462, 476, 486, 489, 493, 496, 499, 502, 506, 509, 512, 515, 518, 521, 524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/data/util/traverse.py 21 14 33% 39-56 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/__init__.py 25 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/analyzer_cli.py 581 520 10% 84-127, 151-158, 173-418, 444-458, 473-476, 479, 502-598, 616-639, 656-674, 690-738, 757-823, 834, 850-872, 891-908, 925-1050, 1069-1086, 1089-1095, 1107, 1113-1168, 1183-1240, 1244-1271, 1301-1361, 1404-1469, 1495-1520, 1533-1544, 1556-1579, 1601-1659 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_config.py 81 60 26% 41-50, 53-55, 71-97, 114-118, 121, 124-128, 139-147, 150-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/cli_shared.py 177 136 23% 69-82, 99-110, 126-140, 144-147, 185-208, 229, 248-258, 264-273, 301-383, 405-431, 445-493 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/command_parser.py 207 177 14% 37-40, 43-47, 50, 72-101, 118-148, 164-171, 187, 204-216, 234-240, 259-281, 299-310, 328-339, 359-403, 426-439, 454-468, 486-491, 506-550 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/debugger_cli_common.py 451 353 22% 44-45, 49, 73-77, 94-107, 110, 123-132, 145-151, 198-212, 217, 221, 225, 228, 246-267, 285-300, 310-332, 343-345, 348, 359-362, 373-375, 403-431, 456-527, 563-585, 625-655, 682-716, 727, 741-757, 767, 783-790, 793-795, 807-812, 825-843, 850, 883-896, 909-915, 929-934, 948-953, 974-981, 992-1001, 1019-1023, 1026-1040, 1043-1047, 1051, 1063-1075, 1088, 1103-1105, 1125-1127, 1131, 1135, 1139, 1142, 1145, 1148, 1161-1162, 1170, 1173, 1176, 1179, 1194-1199, 1221-1248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/evaluator.py 52 37 29% 69-103, 115-116, 131-152 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/profile_analyzer_cli.py 302 252 17% 58-77, 103-131, 134, 137, 140, 143, 173-195, 209-220, 236-380, 396-439, 447-474, 504-575, 586-592, 613-733, 737-742, 756-762, 765, 786-802 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/tensor_format.py 240 215 10% 67-69, 103-199, 233-279, 321-403, 407-426, 449-481, 485, 503-568 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/cli/ui_factory.py 23 16 30% 51-70 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/check_numerics_callback.py 122 89 27% 103-106, 114-119, 156-212, 216, 226-232, 242-289, 314-328, 414-419, 437-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/common.py 22 11 50% 44, 59-71, 86 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_data.py 530 403 24% 53-56, 70-71, 74-76, 80, 99-102, 121-140, 144-148, 152-156, 160, 164, 168, 182, 199, 219-231, 241-242, 252-254, 266-268, 307-330, 334, 341, 350, 361, 375, 385, 395, 405, 415, 425, 435, 442, 455, 485-498, 502-522, 555-576, 580-581, 584-590, 594-598, 602-606, 621-623, 636-655, 669-673, 684, 717-718, 723-730, 739, 748, 771-799, 802-809, 812-817, 834-886, 906-918, 922, 933-935, 953-956, 970-971, 985-986, 1007-1020, 1036-1046, 1062-1066, 1085-1093, 1121-1137, 1141-1151, 1193-1229, 1249-1258, 1266, 1284-1295, 1313-1322, 1339-1344, 1363-1378, 1397-1415, 1448-1464, 1490-1497, 1522-1528, 1560-1567, 1594-1601, 1618-1625 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_events_writer.py 52 29 44% 51-54, 64-66, 76-79, 89-92, 102-104, 113-115, 125-128, 132, 137, 146, 150, 154, 157-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_gradients.py 120 82 32% 38-39, 53-65, 85-98, 102, 106, 109, 112, 157-169, 215-222, 267-284, 287-290, 304-306, 324-329, 338, 341-346, 353, 360-363, 369, 403-417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_graphs.py 237 183 23% 40-46, 50-51, 66-67, 83, 98, 116-139, 170-178, 191-214, 217, 220, 225-234, 241-266, 277-309, 320-329, 333-337, 346-354, 358-365, 372-393, 401-405, 413-431, 435, 440, 445-446, 450, 454, 458, 462, 466, 470, 474, 478, 503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/debug_utils.py 69 60 13% 61-79, 137-197, 252-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/dumping_callback.py 301 237 21% 61, 67-68, 73, 77, 89-115, 125-133, 137, 141-143, 147, 151, 155-159, 176-189, 202-205, 216-232, 242-267, 288-301, 334-421, 453-514, 526-571, 590-608, 611-617, 632-643, 740-807, 819-826 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/op_callbacks_common.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/profiling.py 41 25 39% 44-56, 62, 76-80, 90-100, 104, 108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/lib/source_utils.py 135 110 19% 44, 48-49, 53-54, 58, 78-84, 112-125, 145-158, 192-225, 262-325, 353-383 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/dumping_wrapper.py 42 28 33% 69-90, 110-135 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/framework.py 279 191 32% 130-131, 148-149, 177-178, 204-209, 259-270, 298-305, 314, 346-379, 383, 387, 391, 395, 428-517, 530-588, 605-634, 640-641, 645, 650, 654, 658, 661, 667-676, 679-684, 688, 691, 703, 733-734, 753, 806, 809-811, 814, 818-819, 822, 828-831, 867-874, 878, 915-923, 928, 948-951, 975-984, 989 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/grpc_wrapper.py 58 38 34% 57-66, 100-118, 137, 140, 146-151, 155-159, 193-210, 220-224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/hooks.py 95 67 29% 60-65, 83-86, 89, 92-141, 146-148, 176-180, 183, 186-217, 220, 256-264, 277-301, 335-349, 352-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/debug/wrappers/local_cli_wrapper.py 237 197 17% 80-131, 136, 139-206, 218, 230, 242-277, 280-285, 289-304, 320-374, 377-378, 399-446, 449-453, 462-468, 471-485, 488-513, 525-576, 579-603, 613, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/all_reduce.py 392 355 9% 44-57, 71-75, 95-128, 145-157, 176-190, 223-251, 277-294, 317-356, 370-374, 394-423, 469-477, 496-518, 532-555, 582-589, 608-626, 640-645, 666-682, 701-711, 730-762, 767-776, 781-785, 790-791, 797-800, 820-842, 848-853, 860-865 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/central_storage_strategy.py 34 11 68% 56-70, 75, 103, 144, 162, 180, 246, 255-260 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/cluster_resolver.py 173 108 38% 36-39, 44-56, 99, 117, 144-154, 172, 183-198, 202, 218-223, 227, 231, 235, 239, 243, 262-265, 269, 273, 305-321, 347-395, 411-415, 419, 423, 427, 431, 435, 441, 446, 450 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py 77 49 36% 30-31, 83-104, 116-149, 152-162, 166, 170, 174, 180, 184, 188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py 52 34 35% 29, 76-94, 112-120, 135-158 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py 160 132 18% 39-86, 96-107, 121-125, 135, 146-155, 164, 233-275, 280, 284, 288, 298-301, 321-358, 372, 386-394, 401-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py 78 42 46% 36-39, 43, 47-48, 76-79, 83-87, 91-95, 99, 103, 107, 111-114, 118, 124-126, 135-138, 160-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py 106 67 37% 40, 76-85, 90-95, 150-167, 170, 173, 201-214, 217, 220, 246-256, 278-300, 305, 308-319, 324 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_all_reduce_strategy.py 228 165 28% 99-110, 116-118, 135, 148-158, 169-176, 180-183, 187-242, 247-344, 356-391, 399-408, 411-412, 421-422, 434-435, 456-470, 473-508, 511-530, 537-540, 545, 549, 553, 557, 561, 565, 577 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/collective_util.py 11 3 73% 62-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_ops.py 452 356 21% 56-59, 64-73, 79-102, 107-116, 122-138, 144-151, 157-161, 165, 169-174, 179-188, 194-211, 219, 224, 251-266, 298-318, 331-332, 359, 387, 402, 426-428, 432-443, 447, 472-479, 504-513, 529-531, 535-583, 587-617, 622-628, 633-635, 652-655, 659-663, 668-672, 679-690, 695-720, 724-730, 760-764, 791-795, 835-861, 867-907, 948-953, 957, 961-989, 993-1004, 1012-1024, 1031-1101, 1106-1137, 1151-1183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/cross_device_utils.py 400 345 14% 44-53, 80-135, 158-170, 188-197, 214-229, 270-275, 280-282, 293-306, 318-319, 323-325, 329-331, 365-387, 414-433, 465-535, 545-576, 594-618, 635-655, 675-690, 705-713, 743-769, 785-801, 806-809, 813-818, 822-830, 835-842, 846-849, 863-874, 887-897, 910-916, 937-958, 976-986 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/device_util.py 47 29 38% 47-67, 72, 79-80, 87-90, 93, 96, 102-108, 113-114, 121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator.py 315 243 23% 74-79, 83-92, 97-99, 137-146, 149-153, 156-162, 167, 172-192, 196-205, 213-216, 244-260, 268, 273, 278, 283, 288, 293, 298, 303, 308, 313, 318, 323, 336-360, 373-382, 395-451, 457-494, 500-526, 538-552, 590-625, 752-868 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_coordinator_context.py 11 4 64% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py 687 352 49% 149-152, 159-160, 163-164, 167-168, 186-194, 206, 209-214, 220-225, 232-240, 249, 254-258, 277-285, 289-300, 303-332, 374-376, 381, 386, 391, 407-411, 414, 463-464, 469, 474, 477, 507, 617-625, 640-644, 661, 666, 673-677, 705, 711-713, 874, 943, 957, 998-1056, 1078, 1097, 1102, 1107, 1126, 1132, 1136-1144, 1147, 1201, 1264, 1314, 1388, 1429, 1470, 1501, 1536, 1540, 1559, 1754-1777, 1785, 1789, 1818, 1857-1865, 1869, 1872, 1877, 1881, 1885, 1888, 1891, 1894, 1897, 1901, 1922-1931, 1934, 1953-1959, 1964, 2007-2013, 2016, 2033-2039, 2042, 2045, 2060, 2064-2072, 2077, 2082, 2089, 2095, 2110, 2118, 2121, 2140, 2165-2166, 2169, 2182-2186, 2189, 2233-2235, 2240, 2293, 2308, 2317, 2322, 2327, 2332, 2374, 2415-2420, 2424-2429, 2434, 2447-2448, 2458-2459, 2489-2511, 2526-2530, 2544, 2550-2551, 2571, 2583-2584, 2603-2604, 2613, 2616, 2619, 2624-2625, 2628-2637, 2640-2643, 2653-2654, 2659, 2664-2669, 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870-957, 966, 975-977, 982-990, 994, 1001-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/tpu_values.py 143 89 38% 41-46, 51-61, 68-75, 78-81, 85-88, 92, 95-98, 101-104, 108, 114-118, 124, 127-129, 132-135, 138-141, 144-147, 151, 159-166, 171-183, 190-196, 200-202, 205-207, 210-212, 215, 218, 221, 224, 227, 230, 233, 236, 243-246, 251-254, 259-262, 266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/values.py 749 470 37% 48-55, 125, 129-133, 136, 142-151, 156, 160, 163-165, 168-170, 191-198, 203, 212, 216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300-304, 307-311, 314-318, 321-325, 335, 341, 352, 355, 359, 362-367, 370, 380, 383-387, 391-392, 396-397, 401-402, 416-433, 445-454, 458-465, 468, 472, 476, 480, 484, 488, 493, 497, 501, 505, 509, 513-518, 521, 525, 528, 531-532, 536, 540, 544, 547, 550, 557-560, 564, 567-568, 571, 575, 582-584, 591-595, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features.py 36 18 50% 92, 102, 111-113, 116, 137-150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/dense_features_v2.py 18 6 67% 82-87, 90-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/feature_column/feature_column.py 877 622 29% 182-231, 297, 322-329, 332, 342, 346, 350, 354, 358, 362, 366, 492-504, 516-526, 543-548, 551-566, 569-577, 590-592, 595-601, 604, 608-612, 616, 633-661, 669, 672-700, 706-708, 744-752, 801-814, 904-929, 997-1009, 1083-1097, 1144-1155, 1242-1268, 1357-1385, 1444-1453, 1488, 1558-1560, 1670-1688, 1718-1722, 1731, 1734-1742, 1745, 1768, 1790, 1810, 1815, 1840, 1861, 1885, 1910, 1921-1931, 1947-1963, 1981, 2008, 2046-2067, 2085, 2128-2129, 2149-2171, 2191-2217, 2226-2233, 2254-2274, 2297-2323, 2335, 2339, 2346-2353, 2357, 2374-2378, 2388, 2392, 2395-2396, 2402, 2406-2409, 2418, 2423-2448, 2470, 2484-2486, 2490, 2493, 2497-2499, 2507-2531, 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705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/cpp_shape_inference_pb2.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device.py 57 8 86% 29, 42, 53, 55, 95, 117, 120, 169 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/device_spec.py 156 25 84% 46, 210, 333, 339, 344, 389, 403-404, 408-409, 413-414, 418-419, 423-424, 427-429, 432-436, 439-442, 453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/dtypes.py 215 49 77% 79-82, 88, 95-101, 116-130, 140-154, 167-170, 200-201, 216, 628-629, 633-642 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/error_interpolation.py 202 168 17% 86-98, 119-140, 145, 168-189, 195, 214-223, 243-251, 256-257, 274-281, 299-337, 372-401, 416-425, 438-452, 467-484, 502-542 /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/errors_impl.py 210 72 66% 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63-64, 96-126, 152-174, 179, 185-191, 244-247, 250-251, 265, 269, 285-304, 311-316, 320-325, 328-330, 333-335, 347-350, 363-365, 377-379, 388-390, 400-402, 411-413, 424-425, 435-436, 445-447, 456-458, 467-469, 478-480, 505, 601, 615, 771-772, 775-776, 779-793, 796-803, 812-817, 839-855, 858-865, 869, 872-873, 876, 879-881, 884, 887-888, 891, 894, 897-898, 901, 904-905, 909-928, 931-935, 952, 955-962, 1046-1100, 1103-1108, 1112-1140, 1145-1155, 1158-1163, 1166, 1169-1182, 1191-1231, 1238-1257, 1264-1267, 1315-1358, 1362, 1427-1457, 1461-1466, 1469-1485, 1488-1489, 1492-1498, 1529-1535, 1538-1559, 1594-1608, 1612-1613, 1709-1739, 1743-1765, 1770-1801, 1806-1840, 1847-1850, 1865-1869, 1873-1886, 1890-1899, 1905-1909, 1928-1947, 1950-1952, 1955, 1968-1983, 1988-1990, 1993, 1997-2003, 2006-2018, 2026-2030, 2038-2039, 2046-2054, 2062-2063, 2073-2106, 2110-2122, 2126-2146, 2149-2151, 2200-2220, 2225-2237, 2240, 2243-2271, 2274, 2299-2311, 2314-2321, 2326-2363, 2366-2367, 2431-2456 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/callbacks_v1.py 218 186 15% 130-163, 167-175, 180-230, 235-310, 313-314, 325-343, 351-367, 370-372, 378-385, 394-451, 454-457 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/constraints.py 102 47 54% 38, 41, 68-69, 72-75, 78, 87, 109, 112, 118, 152-155, 158-163, 166, 204-216, 221-249, 268, 273, 284-292 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/boston_housing.py 25 16 36% 58-79 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar.py 19 12 37% 37-51 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar10.py 31 19 39% 50-82 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/cifar100.py 27 15 44% 59-84 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/fashion_mnist.py 26 15 42% 66-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/imdb.py 56 42 25% 99-158, 171-177 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/mnist.py 15 6 60% 57-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/datasets/reuters.py 47 33 30% 106-151, 164-170 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/distribute/distributed_training_utils.py 473 386 18% 68-80, 113-137, 142-166, 193-213, 236, 253-272, 308-322, 343-359, 363-366, 372-375, 381-399, 404-410, 424-426, 435, 447-449, 459-469, 502-573, 577-581, 585-588, 592-595, 600-620, 636-669, 684-689, 694-703, 732-773, 779-783, 790-825, 830-835, 840-849, 854-880, 885-902, 907-913, 918-947, 953-983, 993-1025, 1033-1040, 1045-1049, 1054-1062, 1066-1071, 1075-1076, 1080-1081, 1085-1086, 1090-1091, 1095-1096, 1101-1102, 1121-1133, 1137, 1151-1169, 1176-1192, 1197-1201 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_preprocessing_layer_v1.py 26 14 46% 51-54, 58-61, 66-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/compile_utils.py 318 253 20% 41-44, 63-68, 85-98, 101, 104, 126-132, 136-149, 153-161, 183-247, 261-269, 272, 275, 295-297, 301-335, 342-370, 375-383, 387-416, 420-421, 434-477, 481-484, 488-490, 495, 500, 528-545, 575-592, 597-602, 607-617, 623-632 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/data_adapter.py 639 339 47% 56-57, 61-62, 115, 140, 156, 172, 185, 198, 203, 211, 216, 220-225, 229, 241, 249, 264-371, 388-408, 411, 414, 417, 420, 423, 427, 452, 467, 470-476, 493-523, 533, 539, 542, 546-548, 562-593, 596, 599, 602, 605, 608, 611, 622, 628, 630, 641-650, 660, 663, 666, 669, 672, 675, 691-699, 702, 705, 708, 711, 714, 720, 726-735, 762, 765, 779-780, 789, 801-802, 823, 830-831, 836-849, 855, 858, 861, 864, 867, 870, 891, 894, 917-922, 925-932, 944, 960, 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906-915, 936-952, 1039-1098, 1143, 1146-1151, 1273, 1290-1291, 1340-1359, 1400-1417, 1434-1439, 1464-1465, 1497-1500, 1525-1526, 1542, 1544, 1548-1549, 1559, 1568, 1572, 1576, 1581-1584, 1600, 1624-1627, 1634-1635, 1641, 1645, 1649-1660, 1663-1669, 1677-1688, 1691, 1701, 1721-1729, 1737, 1739, 1744, 1750-1760, 1785-1814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_arrays.py 259 222 14% 42-43, 126-458, 462-467, 471-477, 482-484, 501-535, 539-542, 546-550, 555-557, 564-583, 621-649, 678-687, 705-708 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_distributed.py 322 282 12% 46-47, 52-56, 73-120, 164-290, 315-420, 444-574, 599-672, 698-720, 737-754, 766-779, 786, 789, 793, 798 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_eager.py 119 98 18% 37-39, 55-82, 114-219, 250-283, 308-322, 349-366 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_generator.py 239 198 17% 123-336, 349-362, 398-418, 449-484, 493-509, 514-530, 535-538, 571-574, 604-606, 626-627, 659-666, 692-695, 706-707, 738-766, 791-800, 816-819 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_utils.py 834 682 18% 79-83, 92, 105, 110, 124, 131, 135-140, 143-145, 156-157, 161, 168-174, 178-192, 209-212, 241-244, 253-258, 262-286, 289-300, 303-310, 321-342, 345-347, 350-353, 358-364, 385-400, 428-436, 441-457, 486-583, 601-626, 633, 638, 657-697, 713-750, 771-808, 844-891, 907-915, 944-1042, 1046-1048, 1052-1056, 1069-1091, 1106-1133, 1142-1157, 1162-1186, 1210-1213, 1216-1228, 1233-1235, 1256-1270, 1278-1289, 1297-1306, 1333-1355, 1359-1364, 1380-1391, 1405, 1420-1421, 1435-1461, 1482-1503, 1525-1544, 1552, 1556, 1584-1613, 1639-1670, 1682-1689, 1694, 1700-1705, 1709-1711, 1723-1725, 1737-1759, 1787-1817, 1827-1840, 1850, 1857-1887, 1891-1892, 1896, 1923-1925, 1929, 1947, 1951, 1965-1970, 1975-1977, 1997-2007, 2012-2030, 2049-2078, 2110, 2123, 2133 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training_v1.py 1067 853 20% 71-72, 143-163, 167, 175-180, 228-233, 301-463, 475-476, 481-486, 494-506, 522-545, 549, 556-586, 754-766, 874-879, 953-957, 970-976, 1025-1070, 1110-1146, 1166-1192, 1217, 1249-1251, 1276, 1288-1299, 1313-1337, 1348-1350, 1362-1380, 1386-1423, 1427-1437, 1460-1468, 1471-1478, 1494-1515, 1528, 1532, 1546-1627, 1632-1642, 1646-1650, 1673-1742, 1747-1756, 1760-1768, 1791-1799, 1805, 1818-1827, 1831-1856, 1876-1882, 1914-1938, 1949-1953, 1959-2003, 2006-2030, 2033-2041, 2049-2057, 2099-2170, 2242-2302, 2312-2436, 2440-2498, 2501-2534, 2572-2591, 2596-2636, 2643-2647, 2652, 2660, 2668, 2676, 2684, 2692, 2696-2702, 2706, 2710, 2714, 2732-2735, 2743-2746, 2753-2754, 2774-2775, 2781-2787, 2791, 2794-2802, 2806, 2813-2814, 2817, 2820, 2825-2829, 2832-2835, 2842-2846, 2886-2893, 2897, 2901, 2905, 2909, 2913, 2917, 2921, 2925, 2942-2972, 2979, 2983, 2987, 2991, 2995, 2999, 3002, 3005, 3009, 3012, 3016-3018, 3023-3042, 3048, 3054-3075, 3097-3099, 3103, 3107, 3111, 3115, 3135-3151, 3165-3175, 3179-3180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/estimator/__init__.py 23 12 48% 115-122, 212-219 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/initializers.py 89 8 91% 93, 118, 142, 166, 181, 194, 202, 207 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/__init__.py 180 21 88% 54-59, 157-159, 214-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/advanced_activations.py 135 78 42% 68-70, 73, 76-78, 82, 126-136, 140-157, 160-162, 165-172, 176, 203-205, 208, 211-213, 217, 244-246, 249-250, 253-255, 259, 279-281, 284, 287-289, 293, 345-358, 363, 369-375, 379 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py 730 587 20% 121-149, 152-191, 194-222, 225-250, 254-273, 277-282, 285-288, 291-295, 298-304, 319-325, 429, 582, 726, 874-898, 903-934, 937-994, 997-1027, 1030-1032, 1167-1190, 1195-1226, 1229-1295, 1298-1332, 1335-1338, 1430-1453, 1456-1496, 1499, 1502-1543, 1656, 1680-1719, 1841, 1866-1887, 1985-2001, 2004-2036, 2039-2056, 2060-2078, 2081-2093, 2134-2136, 2139-2141, 2144-2145, 2148-2150, 2216-2223, 2226-2239, 2243, 2248-2254, 2302-2305, 2308-2325, 2329, 2333-2335, 2384-2386, 2389-2393, 2396, 2399-2401, 2472-2492, 2495-2516, 2520, 2524-2526, 2584-2610, 2613-2642, 2646, 2650-2652, 2692-2694, 2697-2702, 2705-2708, 2711-2713, 2768-2788, 2791-2802, 2813-2833, 2838-2840, 2899-2925, 2928-2958, 2964-3014, 3020-3022 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional_recurrent.py 345 261 24% 165-181, 185-222, 228-275, 279-292, 302-350, 353-420, 510-537, 541-584, 587-644, 647-654, 657-660, 663-692, 842-869, 872-873, 880, 884, 888, 892, 896, 900, 904, 908, 912, 916, 920, 924, 928, 932, 936, 940, 944, 948, 952, 956, 960, 963-995, 999 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/core.py 458 303 34% 101-104, 107, 110-115, 118, 121-123, 179-183, 189-196, 199-212, 215, 218-224, 260-261, 264-266, 311-318, 321-325, 369-376, 379-383, 415-417, 420, 423, 426-428, 472-473, 495-515, 518-527, 530, 534-536, 571-578, 581-586, 589, 592-594, 630-632, 635-667, 670-679, 682-684, 714-716, 719-720, 723, 726-728, 825-841, 845-870, 874-890, 893-932, 937, 940-942, 945-966, 969-985, 989-1016, 1022-1048, 1129, 1150, 1154, 1176, 1183-1188, 1192, 1199, 1202-1208, 1211-1225, 1246-1250, 1253, 1256-1258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/cudnn_recurrent.py 189 143 24% 65-81, 84-121, 124-133, 137, 141-143, 147-149, 153, 156, 215-236, 240, 243-269, 272-316, 319-337, 400-422, 426, 429-465, 468-518, 521-540 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/dense_attention.py 132 95 28% 77-80, 92, 119-134, 138-167, 170-176, 180-196, 201-206, 307-308, 312-321, 332-335, 338-340, 443-444, 447-460, 473-480, 484-486, 491-495, 499-503 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/embeddings.py 71 46 35% 101-123, 133-148, 151-154, 158-178, 181-185, 188-203 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/kernelized.py 80 56 30% 138-154, 157-200, 203-207, 210-216, 219-228, 234-251, 255-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/local.py 209 172 18% 151-171, 175-259, 263-274, 277-298, 301-334, 465-485, 489-581, 585-600, 603-625, 628-661, 704-724, 770-778, 807-816, 834-841 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py 342 244 29% 49, 67-87, 92-118, 122, 124-181, 187-202, 205-217, 251-254, 284-286, 290-293, 320-323, 357-360, 387-390, 417-420, 493, 496, 504-519, 526-536, 541-564, 567-571, 639-654, 659-674, 680-700, 704-722, 725, 728-733, 767, 796, 810, 846, 878, 892, 927, 947 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/noise.py 80 47 41% 60-62, 66-73, 76-78, 82, 111-113, 116-127, 130-132, 136, 170-174, 177, 180-203, 206-208, 212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization.py 511 442 14% 199-247, 258-271, 275-279, 283, 287-289, 292-295, 300-303, 306, 311-503, 506-516, 519-521, 525-635, 640-693, 696, 699-708, 711-722, 725-890, 893, 896-928, 1010-1034, 1042-1055, 1058-1105, 1109-1193, 1196, 1199-1212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/normalization_v2.py 43 23 47% 136, 161-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/pooling.py 256 162 37% 60-70, 73-81, 84-98, 101-108, 193, 235, 272-282, 285-297, 300-315, 319-326, 458, 508, 544-554, 557-574, 577-596, 600-607, 654, 704, 714-717, 720-724, 727, 730-732, 775-777, 780-789, 792, 840-841, 849-852, 855-859, 862, 865-867, 906-909, 947-950, 957-960, 963-967, 970, 973-975, 1008-1011, 1043-1046 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding.py 218 164 25% 83-129, 136, 139-145, 163-170, 173-178, 181-187, 190-193, 196-205, 208-217, 220-234, 237-292, 317-318, 322-341, 345-364, 379, 394-412, 416, 421-429, 433-447, 452-458 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/categorical_encoding_v1.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/image_preprocessing.py 485 384 21% 78-83, 86-89, 92-96, 99-100, 104-110, 135-138, 141-144, 147-171, 174-175, 179-184, 214-219, 222-277, 280-281, 285-291, 315-316, 319-320, 323, 326-330, 367-383, 386-402, 405, 408-413, 467-506, 509-541, 544, 547-555, 570-578, 647-670, 696-704, 768-790, 793-816, 819, 822-829, 879-918, 921-952, 955, 958-966, 987-997, 1043-1054, 1057-1067, 1070, 1073-1078, 1118-1132, 1135-1157, 1160-1161, 1165-1171, 1213-1227, 1230-1252, 1255-1256, 1260-1266, 1270-1273, 1277-1282 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup.py 230 173 25% 104-190, 193-208, 211-212, 215, 218-222, 225-231, 234-235, 239-241, 245-246, 250-257, 260, 263-268, 283-285, 288-294, 297-305, 312, 333-353, 356-358, 361-389, 392-401, 404-429, 451, 455-465, 469-477, 489-495, 499, 504-507, 511-518, 523-524 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/index_lookup_v1.py 38 22 42% 63-66, 69, 72-76, 79-81, 84-85, 89-95 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization.py 95 56 41% 63-71, 75-104, 109-111, 114, 117, 120-122, 126-128, 149, 155-173, 178-197, 202, 212-220, 225-230, 234-235, 241 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/normalization_v1.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization.py 309 235 24% 214-320, 325, 328-329, 332, 336-338, 342-343, 347, 350-359, 362-364, 379-404, 407, 410-420, 427, 461-521, 528-534, 537-543, 546-592, 595-633, 663-665, 669-694, 698-715, 730, 745-762, 766, 771-781, 785-802, 807-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/preprocessing/text_vectorization_v1.py 26 9 65% 84, 87, 91-97 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent.py 1046 802 23% 85-104, 108, 113-118, 121-131, 135-160, 165-180, 183-191, 195-200, 397-439, 444-447, 454, 457-505, 513-519, 522-592, 606-621, 625-645, 648-707, 717-814, 820-854, 857-866, 871-873, 890-943, 946-965, 969-974, 978, 1052, 1061, 1066, 1069, 1096-1099, 1110, 1121, 1124, 1131, 1153-1156, 1174-1177, 1266-1290, 1294-1319, 1322-1340, 1343, 1346-1378, 1489-1526, 1529-1530, 1535, 1539, 1543, 1547, 1551, 1555, 1559, 1563, 1567, 1571, 1575, 1579, 1583, 1587, 1590-1625, 1629-1631, 1709-1740, 1744-1778, 1781-1879, 1882-1907, 1910, 2032-2071, 2074-2075, 2080, 2084, 2088, 2092, 2096, 2100, 2104, 2108, 2112, 2116, 2120, 2124, 2128, 2132, 2136, 2140, 2144, 2147-2188, 2192-2194, 2275-2312, 2316-2353, 2357-2367, 2371-2376, 2379-2436, 2439-2477, 2480, 2518-2530, 2536-2549, 2552-2559, 2678-2717, 2720-2721, 2726, 2730, 2734, 2738, 2742, 2746, 2750, 2754, 2758, 2762, 2766, 2770, 2774, 2778, 2782, 2786, 2790, 2793-2834, 2838-2840, 2844-2852, 2877-2913, 2918, 2923-2926, 2931-2944, 2964-2991, 3008-3011 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/recurrent_v2.py 321 259 19% 165, 345-385, 388-398, 403-454, 462-503, 547-587, 593-673, 713-791, 895, 1059-1102, 1107-1207, 1233-1238, 1288-1321, 1359-1444, 1485-1569, 1596-1602, 1625-1626, 1631-1635, 1642-1645, 1649-1650 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py 43 17 60% 42-43, 66, 71-72, 75-82, 86-89, 98-100, 113, 124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/serialization.py 57 18 68% 68, 84-105 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/wrappers.py 370 318 14% 52-54, 57-60, 64-67, 70-77, 81-86, 126-137, 163-172, 175-184, 187-195, 199-251, 290-327, 400-456, 460-469, 480-495, 499-518, 522-593, 602-677, 680-681, 684-688, 691-707, 711-715, 718-728, 733-745 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/losses.py 266 127 52% 105, 107, 140-144, 157, 161, 177, 181-201, 243-246, 249-253, 312, 372, 433, 494, 570-576, 729, 793, 855, 916, 974, 1032, 1093, 1162, 1196-1198, 1228-1230, 1262-1266, 1300-1304, 1309-1319, 1347-1350, 1379-1382, 1411-1415, 1438-1444, 1481-1487, 1517-1527, 1555-1557, 1585-1594, 1632-1636, 1667-1669, 1714-1716, 1782, 1797-1803, 1816, 1831, 1853-1863 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/metrics.py 759 457 40% 151, 164-167, 186-207, 212, 216, 224, 244, 253, 268, 274, 290, 328-374, 377-385, 425, 516-518, 533-547, 551-554, 595-604, 608-618, 624-628, 665, 710, 765, 809, 889, 913-918, 936, 944-948, 951-952, 956-958, 1004, 1054, 1104, 1154, 1225-1237, 1256, 1269-1271, 1274-1275, 1279-1285, 1351-1363, 1382, 1395-1397, 1400-1401, 1405-1411, 1423-1450, 1465, 1478-1479, 1537-1541, 1546-1556, 1561-1566, 1623-1627, 1632-1642, 1647-1652, 1701-1705, 1713-1723, 1728-1730, 1782-1786, 1796-1807, 1810-1813, 1920-1981, 1985-2023, 2038-2067, 2129-2164, 2167-2214, 2219-2223, 2228-2244, 2295, 2326, 2359, 2390, 2423, 2456, 2491, 2523, 2555, 2570-2575, 2579, 2610, 2641, 2672, 2729-2734, 2754-2776, 2780-2799, 2803, 2806-2808, 2840-2844, 2847-2857, 2861, 2865, 2877-2908, 2911-2916, 2919-2920, 2973, 3112, 3136, 3155-3157, 3160-3166, 3170-3174, 3178-3184, 3200-3202, 3219, 3239-3252, 3268, 3285-3294, 3310-3312, 3327-3330, 3335, 3340, 3345, 3355-3364, 3368 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/autocast_variable.py 243 135 44% 63-69, 73-75, 82-85, 90, 93-96, 99-100, 104-105, 109-110, 113, 117-127, 131, 134-143, 161, 165, 169, 173, 176, 179, 183, 187, 190-191, 194-195, 198-199, 202-203, 206-207, 210-211, 214-215, 218-219, 222-223, 226-227, 230-231, 234-235, 238-239, 242-243, 246, 250, 254, 258, 262, 266, 270, 274, 277, 284, 289, 292, 304, 308, 312, 316, 325, 328, 331, 334, 337, 340, 343, 346, 349, 352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388-392, 395-399, 402-406, 409-413, 439-462, 481-483 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/device_compatibility_check.py 66 48 27% 54-61, 73-128, 154-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/get_layer_policy.py 11 3 73% 38-41 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale.py 15 4 73% 29, 33-38, 48 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/loss_scale_optimizer.py 140 81 42% 48, 123-155, 160, 182-189, 212-214, 218-224, 227-229, 232, 238-245, 251-272, 279, 284-286, 293-298, 301-303, 313, 317, 320, 323, 327, 330, 333, 336, 345, 349, 353, 357, 366, 372, 395-405 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/mixed_precision/experimental/policy.py 133 60 55% 328, 331, 339, 341, 348, 361-371, 374, 376, 378, 382-388, 460, 463-471, 475-479, 508-509, 519-520, 549-558, 573-578, 582-586, 606, 612-617, 621-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/models.py 277 231 17% 57, 61, 67-74, 90-129, 162-215, 234-247, 265-276, 307-381, 419-426, 452-533, 550-557, 570-589, 638-723 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adadelta.py 49 28 43% 100-104, 108-111, 114-115, 121-127, 130-136, 147-153, 165-172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adagrad.py 60 35 42% 91-100, 103-107, 110-111, 118-124, 143-147, 150-155, 164-169, 179-186 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adam.py 84 61 27% 144-150, 155-161, 164-173, 185-192, 195-217, 232-269, 272-281 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/adamax.py 63 41 35% 103-108, 112-115, 118-126, 137-144, 157-181, 184-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/ftrl.py 60 42 30% 108-136, 141-146, 149-150, 162-181, 194-214, 228-245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/gradient_descent.py 53 24 55% 112, 118-120, 123-124, 128-143, 148-156, 161-166, 177-184 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/learning_rate_schedule.py 249 184 26% 44, 48, 60, 135-140, 143-154, 158, 225-233, 236-256, 259, 360-367, 370-392, 399, 480-486, 489-502, 505, 573-578, 581-594, 597, 668-675, 678-716, 719, 803-810, 813-831, 835, 925-934, 937-965, 969, 983, 988 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/nadam.py 96 72 25% 92-105, 108-124, 127-146, 164-165, 168-188, 191-228, 231-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py 448 310 31% 72-76, 268, 272, 293, 301, 333-336, 340-356, 385-402, 418-430, 472-504, 520-545, 550-604, 607-613, 618, 622-630, 633-643, 646, 661-663, 675, 677, 684, 686, 692, 696-730, 733-735, 738-754, 757-759, 763-765, 768-782, 787-795, 799-803, 807-815, 829-834, 853-859, 863-870, 874, 879, 908-909, 941-958, 969-1000, 1004, 1019-1023, 1034, 1041, 1055, 1080-1082, 1104, 1107-1109, 1112-1114, 1119, 1124, 1132-1140, 1163-1199, 1206-1224, 1243-1247, 1253-1254, 1269-1270, 1274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizer_v2/rmsprop.py 101 76 25% 133-146, 149-156, 159-162, 171-214, 217-272, 275-281, 284-293 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/optimizers.py 434 336 23% 62-72, 88, 91, 107-121, 137-151, 159, 162-167, 171, 189-196, 199-202, 205-230, 233-240, 258-267, 270-272, 275-297, 300-307, 332-340, 343-346, 349-371, 374-380, 411-420, 423-427, 430-459, 462-469, 495-506, 509-516, 519-555, 558-567, 590-600, 604-610, 613-644, 647-655, 681-691, 694-699, 702-742, 745-753, 760-767, 772, 775, 778, 781-806, 810, 813, 816, 832, 849-865, 892-902 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/linear.py 59 39 34% 88-96, 99-127, 130-147, 150-160, 164-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/premade/wide_deep.py 98 75 23% 87-91, 94-109, 113-136, 140-194, 197-205, 209-215 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/image.py 95 29 69% 27-28, 80-87, 152-154, 230-238, 299-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/sequence.py 18 1 94% 156 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/preprocessing/text.py 19 2 89% 42-43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/regularizers.py 65 23 65% 152, 172, 192, 215-222, 225, 244, 280, 285, 290, 302, 304-311, 315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/hdf5_format.py 354 269 24% 43-44, 82-130, 158-217, 251-258, 272, 287-310, 316, 318, 320, 323-393, 397-404, 446, 468-473, 483-519, 527-572, 585-598, 610-613, 623-644, 661, 665, 682, 699, 729-789, 809-831, 851-856, 880 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/model_config.py 28 14 50% 29-30, 50-55, 86-90, 114-116 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/save.py 45 21 53% 39-40, 113-137, 181-192 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/base_serialization.py 33 10 70% 34, 43, 54, 74, 87-95, 106, 122, 172 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/constants.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/json_utils.py 32 19 41% 38-41, 44, 48-56, 60, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/layer_serialization.py 74 40 46% 36, 41, 48-69, 72, 76, 81-96, 100-105, 111-119, 127, 131, 141, 144, 152, 155-160 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/load.py 460 367 20% 116-137, 143-148, 175-202, 209-215, 219, 223-234, 239-291, 295-313, 317-344, 348-363, 367-393, 401-431, 434-450, 454-466, 470-472, 486-510, 514-520, 523-539, 544-572, 577-579, 609-623, 627-646, 650-654, 662-687, 694-719, 723-725, 730-738, 745-753, 758-778, 786-791, 802-831, 835, 838-841, 848-863, 872-882, 885, 890-902, 915-922, 931-948, 953-958, 966-968, 972 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/metric_serialization.py 18 7 61% 30, 33-40, 43 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/model_serialization.py 27 11 59% 32, 35-39, 42-55, 62 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/network_serialization.py 14 5 64% 30, 33-39 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save.py 29 14 52% 59-81 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/save_impl.py 271 210 23% 72-78, 97-116, 146-197, 201-205, 233-290, 296-302, 309-316, 320-323, 338-363, 375-397, 406-422, 427-434, 437-441, 445-448, 452, 457-491, 495-504, 509-527, 534-537, 540-542, 545-547, 564-567, 572-576, 582-586, 590-594, 603-607, 614-616, 624-626 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/serialized_attributes.py 78 34 56% 145-154, 158-160, 165, 171, 177, 183-186, 190-203, 207-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model/utils.py 112 88 21% 56-96, 101-113, 117-120, 125-128, 150-199, 214-220, 224-230, 234-239, 243-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saved_model_experimental.py 135 89 34% 133-145, 151-155, 160-163, 168-221, 226-227, 231, 255-326, 331-361, 371, 416-430 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/saving/saving_utils.py 138 110 20% 48-53, 77-87, 91, 112-142, 147-191, 197-199, 204-233, 245-264, 270-275, 280-285, 291-307 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/all_utils.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/conv_utils.py 172 152 12% 29-48, 68-87, 103-113, 128-137, 160-186, 190-197, 201-208, 227-233, 279-309, 358-400, 439-456, 475-482 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/data_utils.py 425 321 24% 56-57, 62-64, 69-104, 111-114, 133-160, 214-282, 286-294, 316-325, 342-350, 357-367, 370, 373, 376-384, 389-393, 453, 462, 467, 471-472, 485-486, 506-514, 519-525, 531-533, 538, 559-645, 662, 689-715, 718, 728-738, 743, 753-759, 762-763, 768, 780, 791, 807-808, 819-826, 831-833, 837-860, 872-880, 894-907, 923, 946-947, 958-964, 968-974, 987-1013 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/generic_utils.py 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/mode_keys.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/multi_gpu_utils.py 82 65 21% 31, 35-36, 157-266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/np_utils.py 26 16 38% 49-61, 76-78 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py 203 105 48% 61-64, 83-91, 95-99, 116-152, 178, 181, 226, 231, 248, 251, 266-295, 319, 345, 352, 355-356, 359, 392, 397-404, 408, 427-428, 452, 458-462, 467-472, 478, 481, 485, 490-496, 521 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/version_utils.py 34 4 88% 69, 79-85 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/vis_utils.py 150 125 17% 45-53, 57-59, 64-65, 98-249, 278-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/wrappers/scikit_learn.py 106 77 27% 75-77, 88-106, 117-119, 130-132, 150-168, 181-187, 214-223, 240-242, 263-270, 293-308, 332-333, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/base.py 218 171 22% 105-110, 148, 153, 195-234, 244-246, 250-258, 262-269, 273-279, 282-294, 300-302, 305-314, 376-481, 507-552, 555-569, 573, 578, 582-593 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/convolutional.py 84 21 75% 98, 198-218, 297, 404-424, 504, 612-632, 717, 829, 947-971, 1072-1096, 1164, 1260-1279, 1344, 1434-1453 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/core.py 39 9 77% 98, 173-187, 219, 226, 270-271, 331-332 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/layers.py 46 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/normalization.py 22 4 82% 147, 172, 312-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/pooling.py 78 30 62% 50-52, 90-95, 120-122, 160-165, 194-196, 235-238, 267-269, 308-311, 342-344, 385-388, 419-421, 460-463 /usr/local/lib/python3.8/dist-packages/tensorflow/python/layers/utils.py 129 109 16% 28-47, 67-86, 90-95, 99-103, 119-129, 144-153, 168-175, 197-200, 219-227, 232-233, 238-243, 260-285 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/file_io.py 257 108 58% 58, 66, 71, 76, 84, 120, 139-165, 169-170, 174-181, 189-191, 202, 205-208, 211, 220-221, 232, 316-320, 333-334, 350, 366-374, 396, 412, 458, 474, 526-535, 589-590, 610-614, 653, 681, 701-729, 782-790, 808-814 /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/python_io.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/lib/io/tf_record.py 81 48 41% 90-99, 114-125, 129-149, 170-171, 212, 294-298, 313, 317, 321 /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/module/module.py 96 58 40% 107-121, 130, 135-139, 154, 169, 193, 249-252, 287-291, 295, 299, 303, 310, 314, 326-378 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_grad.py 573 409 29% 44, 50, 72-212, 218, 228, 247-260, 266-285, 301-305, 319, 324-329, 337, 342, 347, 352, 358, 364-368, 374-382, 389-399, 407-424, 430-460, 466-500, 505-507, 516, 525, 531-538, 552-564, 569-585, 593-615, 627-687, 692-700, 705-713, 719, 728, 737, 742, 747, 755, 767, 773, 778, 784-785, 791-792, 810-832, 842-854, 864-865, 876-877, 882-883, 889-890, 898, 906-907, 915, 923-928, 934-939, 947-948, 953-954, 959, 964, 970, 975-1027, 1032-1090, 1095-1097, 1102-1107, 1112-1115, 1120-1123, 1128-1130, 1135-1150 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/array_ops.py 1121 797 29% 193-195, 277, 341-344, 426, 475, 503, 530, 576, 602, 620-630, 649, 683, 715, 732-753, 787, 802-811, 825-836, 840, 902-973, 1037, 1137-1178, 1224, 1270-1277, 1328-1342, 1357-1393, 1407-1414, 1419-1425, 1446-1455, 1501-1511, 1594, 1601-1605, 1659-1693, 1746, 1786-1790, 1832, 1882, 1943-1961, 2042, 2112-2129, 2190-2210, 2368-2371, 2514-2517, 2653, 2661-2667, 2710, 2712, 2721-2723, 2728-2730, 2732, 2774, 2819, 2825-2850, 2883, 2918, 2923-2931, 2959-2981, 3023, 3092-3139, 3199, 3260-3294, 3311-3316, 3362-3392, 3404-3438, 3443-3456, 3519-3527, 3541, 3553, 3565, 3607-3650, 3661-3670, 3678, 3687, 3695, 3704, 3712, 3720-3729, 3845-3848, 3966-4009, 4016-4025, 4064-4090, 4141-4145, 4197, 4243-4251, 4340-4348, 4406-4410, 4424, 4512-4524, 4535, 4554-4560, 4589-4682, 4835-4846, 4852, 4860-4934, 4956-4975, 5007-5019, 5043-5060, 5106-5113, 5172-5184, 5307, 5350-5352, 5405, 5410-5416, 5441-5455, 5499-5565, 5571-5578, 5583-5591, 5641-5644 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/batch_ops.py 25 13 48% 79-111 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/bitwise_ops.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/boosted_trees_ops.py 144 76 47% 63-66, 74-89, 93-95, 111-126, 129, 133, 138-140, 143, 147, 150, 153-156, 159, 162, 176-190, 203-204, 214-227, 234, 238, 245-247, 250, 254-255, 259-262, 271-276, 290, 303 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/candidate_sampling_ops.py 37 12 68% 83-84, 148-149, 208-209, 299-300, 337-338, 386-387 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/check_ops.py 590 425 28% 73-78, 83-85, 91, 238-271, 285-297, 327-372, 392-402, 435, 442-455, 487, 494-506, 539, 547-560, 593, 601-614, 648, 654-658, 696, 705, 758, 810-838, 873, 879, 915, 923, 959, 966, 1003, 1012, 1037-1061, 1094, 1124-1156, 1189, 1222-1255, 1259, 1263-1269, 1294-1321, 1353, 1386-1416, 1436, 1462-1475, 1494, 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870, 873-942, 947-976, 986-1064, 1088-1124, 1138-1148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/confusion_matrix.py 62 42 32% 59-92, 152-201, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_grad.py 121 89 26% 42-88, 98-136, 143, 149-182, 194, 199, 209-232, 237, 243 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/control_flow_ops.py 1318 1123 15% 86-104, 148-176, 189-198, 202-211, 240-259, 274-283, 305-317, 341-362, 390-424, 431-434, 438-442, 450-457, 473-492, 506-529, 545-562, 578-591, 596-618, 641-654, 663-679, 684, 689, 693, 697, 707, 718-724, 727, 732-734, 738-740, 744-745, 749-750, 754-758, 762-764, 768-785, 791-792, 796, 799, 802, 805, 808, 833-849, 858-867, 872, 876, 880, 884-886, 890-892, 895, 906-922, 927-933, 936, 940-972, 975, 979-1032, 1036-1050, 1053-1059, 1063-1083, 1086, 1090-1093, 1175-1296, 1310-1314, 1392, 1397-1401, 1433-1440, 1456-1475, 1484-1529, 1534, 1539, 1544, 1549, 1554, 1559, 1564, 1569, 1580-1609, 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57% 27-30, 52-55, 79-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/custom_gradient.py 189 135 29% 64, 206, 211-214, 251-255, 258, 264-281, 288-298, 304-402, 408-454, 480-507, 551-560 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_grad.py 51 20 61% 33-45, 53-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/data_flow_ops.py 622 469 25% 50-56, 64-88, 92-99, 104-112, 160-182, 200-219, 228, 233-235, 240, 245, 250, 272-293, 304-309, 334-347, 377-395, 412-419, 441-457, 484-500, 528-541, 565-573, 590-595, 606-611, 614-616, 683-706, 754-765, 818-829, 834, 840, 904-919, 974-987, 1055-1075, 1080, 1085-1087, 1106-1108, 1153-1178, 1202-1204, 1219-1221, 1233-1235, 1259-1268, 1273, 1278, 1283, 1294-1297, 1313, 1346-1356, 1375-1379, 1404-1407, 1438-1444, 1464, 1508-1509, 1540, 1564-1566, 1581-1584, 1600, 1618-1647, 1652, 1657, 1662, 1667, 1672, 1677, 1707-1757, 1764-1772, 1790-1800, 1811-1816, 1894, 1915-1933, 1936-1940, 1964-1974, 1994-2004, 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341-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet.py 93 44 53% 196-202, 215, 220, 223, 226, 229, 232, 235-240, 244, 248, 251-252, 255, 258-259, 268, 271-272, 278-280, 284, 292-302, 311-313, 327-329, 351-410 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/dirichlet_multinomial.py 87 43 51% 203-223, 236, 241, 246, 249, 252, 255, 259, 262-278, 282-286, 291, 294, 316-317, 323-325, 331-332, 336-340, 348-351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distribution.py 405 242 40% 91, 135-140, 170, 179, 186, 189, 203, 245, 259, 463-477, 481, 493-495, 515-516, 540-555, 559, 564, 569, 577, 591, 608, 613, 630-631, 634, 649-654, 657, 671, 674, 686-691, 694, 705, 716-717, 730-731, 736, 740-750, 766, 769, 773-782, 795, 798, 802-811, 824, 827, 831-840, 863, 866, 870-879, 898, 901, 906-915, 939, 942, 946-955, 976, 979, 984-985, 988, 993-994, 997, 1001-1004, 1023, 1026, 1048-1055, 1058, 1081-1088, 1091, 1130-1131, 1134, 1139-1140, 1143, 1168-1169, 1172, 1200-1201, 1204, 1220, 1234-1237, 1241-1266, 1271-1310, 1314-1323 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/distributions.py 21 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/exponential.py 44 16 64% 102-116, 120, 124, 127, 130-144, 159-166 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/gamma.py 93 40 57% 165-176, 188, 195, 200, 203, 208, 213, 216, 222, 230, 233-236, 239-240, 243, 247, 254, 257, 260, 267-275, 283-286, 305-314, 330-336 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/kullback_leibler.py 55 26 53% 41-52, 99-121, 159-160, 207, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/laplace.py 87 36 59% 112-119, 130, 137, 142, 145, 149, 153, 156, 159-173, 177, 180, 183, 186, 189-190, 194, 197, 201-202, 205, 208, 211, 214, 217, 234-242 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/multinomial.py 88 46 48% 194-208, 222, 227, 232, 235, 238, 241, 244, 247-277, 281, 284-285, 288-289, 292, 295-297, 303-305, 309-312 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/normal.py 100 46 54% 146-153, 164, 171, 176, 179, 184, 189, 192, 195-198, 201, 204, 207, 210, 213, 216, 219, 223-224, 227, 230, 233, 236, 240-241, 245-246, 263-271, 287-294 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/special_math.py 113 89 21% 137-143, 148-156, 179-185, 195-279, 332-365, 376-379, 384-398, 415-421, 426, 458-474 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/student_t.py 103 52 50% 191-200, 211, 219, 224, 229, 232, 238, 244, 247, 256-266, 269, 272-273, 276, 284-287, 290-294, 306-316, 337-358, 368, 386-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/uniform.py 69 31 55% 114-123, 135, 142, 147, 151-152, 155, 160, 165, 168, 171-175, 178-180, 189-196, 199, 202, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/distributions/util.py 438 377 14% 61-75, 85-86, 93-105, 118-134, 152-161, 193-242, 247, 256, 269, 274-283, 288-292, 297-299, 343-377, 430-485, 513-519, 572-581, 620-657, 686-701, 720-748, 760, 772, 784-787, 792-795, 841-910, 951-979, 1015-1047, 1114-1142, 1164-1199, 1209-1213, 1243-1280, 1308-1351, 1370-1388, 1427-1437, 1443, 1448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/embedding_ops.py 239 200 16% 57-78, 118-249, 314-320, 373, 456-547, 626, 683, 746-826, 854-872, 877-885, 890-894 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/functional_ops.py 275 203 26% 103-161, 230, 296-355, 424, 535-683, 797, 832, 860-861, 867-884, 914-949, 977-1024, 1055-1079, 1118, 1121, 1124, 1127, 1130-1144, 1179 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_array_ops.py 5009 4493 10% 35-62, 68-79, 92-118, 124-133, 146-172, 178-187, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_bitwise_ops.py 319 274 14% 56-95, 101-111, 147-186, 192-202, 238-277, 283-293, 348-387, 393-402, 449-488, 494-504, 524-550, 556-565, 615-654, 660-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_boosted_trees_ops.py 1223 1116 9% 46-80, 86-101, 121-162, 168-192, 241-285, 291-310, 362-409, 415-439, 484-532, 538-566, 588-619, 625-639, 657-681, 686-695, 715-744, 749-761, 781-805, 810-819, 833-869, 875-890, 913-953, 959-977, 996-1028, 1034-1044, 1068-1097, 1103-1113, 1134-1167, 1173-1189, 1215-1259, 1265-1286, 1308-1346, 1352-1369, 1389-1416, 1421-1434, 1452-1480, 1485-1498, 1523-1551, 1556-1567, 1586-1619, 1625-1635, 1649-1685, 1691-1706, 1727-1756, 1762-1772, 1816-1858, 1864-1881, 1934-1981, 1987-2008, 2045-2089, 2095-2115, 2156-2229, 2234-2291, 2338-2442, 2447-2533, 2547-2575, 2581-2591, 2607-2635, 2641-2651 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_candidate_sampling_ops.py 423 383 9% 65-108, 114-133, 169-209, 215-232, 314-400, 406-451, 499-549, 555-576, 624-673, 679-700, 748-799, 805-826, 874-922, 928-949 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_checkpoint_ops.py 103 84 18% 77-119, 125-142, 215-259, 265-284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_clustering_ops.py 116 94 19% 42-68, 74-85, 113-145, 151-164, 194-221, 227-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_collective_ops.py 215 191 11% 38-85, 91-109, 127-173, 179-197, 215-260, 266-284, 305-368, 374-406 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_control_flow_ops.py 434 371 15% 40-68, 73-84, 98-117, 122-127, 151-190, 196-213, 228-254, 260-269, 286-312, 318-327, 354-386, 392-406, 419-445, 451-460, 474-505, 510-515, 539-564, 570, 585-599, 605, 631-650, 656, 669-683, 689, 704-723, 729, 756-770, 776, 802-829, 835-845 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ctc_ops.py 214 186 13% 57-101, 107-127, 164-198, 204-217, 260-315, 321-345, 389-443, 449-472 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_cudnn_rnn_ops.py 910 857 6% 83-145, 151-184, 262-329, 335-364, 446-514, 520-550, 640-721, 727-765, 818-892, 898-948, 1003-1081, 1087-1137, 1183-1247, 1253-1289, 1349-1414, 1420-1456, 1519-1596, 1602-1644, 1711-1773, 1779-1812, 1886-1961, 1967-2007 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py 3989 3627 9% 39-47, 52, 64-78, 84, 101-110, 115, 138-154, 160, 195-236, 242, 265-277, 282, 294-308, 314, 338-347, 352, 364-378, 384, 425-462, 468, 497-528, 534, 547-566, 571-577, 634-678, 684-695, 774-827, 833-857, 885-926, 932, 959-1015, 1021-1051, 1064-1078, 1084, 1096-1122, 1128-1137, 1150-1176, 1182-1191, 1206-1233, 1239-1249, 1266-1306, 1311-1334, 1351-1401, 1407-1433, 1455-1508, 1514-1541, 1558-1608, 1614-1640, 1666-1708, 1713-1740, 1762-1815, 1821-1848, 1876-1930, 1936-1965, 1982-2022, 2027-2050, 2067-2119, 2125-2152, 2175-2229, 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829-849, 883-914, 920-934, 960-986, 992-1001, 1029-1073, 1079-1098 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_image_ops.py 1972 1820 8% 36-64, 70-82, 108-135, 141-151, 173-199, 205-215, 238-264, 270-280, 343-387, 393-414, 470-507, 513-532, 566-596, 602-618, 654-688, 694-711, 764-825, 831-861, 884-913, 919-931, 953-979, 985-994, 1045-1105, 1111-1140, 1172-1206, 1212-1227, 1256-1282, 1288-1298, 1329-1356, 1362-1373, 1423-1498, 1504-1543, 1560-1587, 1593-1604, 1631-1661, 1667-1679, 1736-1782, 1788-1812, 1830-1860, 1866-1878, 1926-1967, 1973-1992, 2014-2053, 2059-2068, 2097-2134, 2140-2157, 2197-2230, 2236-2250, 2292-2322, 2328-2340, 2384-2416, 2422-2435, 2489-2529, 2535-2552, 2616-2657, 2663-2677, 2719-2751, 2757-2771, 2803-2843, 2849-2869, 2904-2943, 2949-2958, 2987-3021, 3027-3043, 3074-3105, 3111-3124, 3146-3184, 3190-3207, 3228-3266, 3272-3289, 3311-3349, 3355-3372, 3393-3432, 3438-3455, 3475-3514, 3520-3537, 3557-3596, 3602-3620, 3709-3789, 3795-3838, 3927-4002, 4008-4048, 4066-4104, 4110-4128, 4146-4185, 4191-4209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_io_ops.py 1163 1009 13% 46-85, 91, 119-181, 187-217, 238-260, 266, 286-320, 326-340, 358-379, 385, 405-444, 450-459, 486-512, 517-527, 543-582, 588-597, 613-627, 633, 648-674, 680-690, 703-717, 723, 735-763, 769-779, 805-820, 826, 854-870, 876, 904-933, 939-950, 976-1004, 1010-1020, 1033-1040, 1045, 1057-1076, 1081-1087, 1106-1114, 1119, 1137-1157, 1162-1169, 1185-1199, 1205, 1220-1246, 1252-1262, 1301-1335, 1341-1355, 1387-1422, 1428-1443, 1487, 1493-1515, 1522, 1534, 1559-1579, 1584-1592, 1635-1657, 1662-1671, 1696-1718, 1723-1732, 1749-1776, 1782-1793, 1807-1834, 1840-1850, 1869-1895, 1901, 1919-1959, 1965-1983, 2003-2030, 2036, 2055-2096, 2102-2120, 2141-2163, 2169, 2189-2223, 2229-2243, 2264-2296, 2301-2308 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_linalg_ops.py 1226 1097 11% 33-59, 65-74, 88-114, 120-130, 143-169, 175-185, 199-229, 235-247, 262-293, 299-312, 328-360, 366-380, 396-431, 437-454, 467-493, 499-508, 529-560, 566-578, 601-639, 645-661, 692-731, 737-746, 769-795, 801-811, 848-880, 886-899, 991-1024, 1030-1045, 1076-1103, 1109-1118, 1161-1205, 1211-1223, 1244-1283, 1289-1298, 1311-1337, 1343-1352, 1382-1425, 1431-1443, 1473-1499, 1505-1514, 1543-1587, 1593-1606, 1663-1694, 1700-1714, 1746-1785, 1791-1800, 1873-1907, 1913-1929, 1968-2012, 2018-2030, 2052-2078, 2084-2093, 2129-2160, 2166-2178, 2221-2258, 2264-2280, 2306-2333, 2339-2349, 2379-2412, 2418-2431 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_list_ops.py 716 641 10% 42-74, 80-91, 119-155, 161-174, 189-218, 224-235, 270-304, 310-322, 339-367, 373-384, 403-431, 437-448, 471-502, 508-520, 540-571, 577-589, 605-631, 637-646, 675-707, 713-724, 744-771, 777-787, 801-828, 834-845, 865-897, 903-914, 932-958, 964-974, 998-1026, 1032-1044, 1067-1096, 1102-1114, 1142-1171, 1177-1190, 1210-1237, 1243-1254, 1277-1305, 1311-1323, 1345-1381, 1387-1401 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_logging_ops.py 492 436 11% 48-63, 68-78, 108-142, 148-162, 193-225, 231-245, 267-293, 299-309, 364-400, 406-422, 445-476, 482-496, 519-560, 566-586, 604-630, 635-647, 665-691, 697-707, 729-773, 779-802, 820-848, 854-865, 884-923, 929-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_lookup_ops.py 714 631 12% 47-78, 84, 110-157, 163-184, 200-208, 213, 247-266, 271, 305-341, 346-363, 379-399, 404-412, 434-452, 458, 479-510, 516-527, 548-564, 570, 590-618, 624-635, 653-661, 666, 683-703, 708-716, 734-742, 747, 764-784, 789-797, 814-834, 839-846, 859-873, 879, 891-917, 923-932, 970-1015, 1021, 1059-1128, 1134-1168, 1195-1227, 1233, 1258-1296, 1302, 1327-1383, 1389-1414, 1441-1489, 1495-1516 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_manip_ops.py 46 31 33% 64-92, 98-109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_math_ops.py 5995 5342 11% 37-63, 69-78, 103-136, 142-157, 172-211, 217-226, 249-288, 294-303, 330-361, 367-377, 397-428, 434-448, 465-491, 497-507, 530-561, 567-580, 612-642, 648-660, 683-714, 720-733, 748-778, 784-797, 827-859, 865-878, 908-940, 946-959, 990-1029, 1035-1044, 1068-1107, 1113-1122, 1153-1192, 1198-1207, 1229-1268, 1274-1284, 1310-1349, 1355-1364, 1403-1437, 1443-1459, 1502-1536, 1542-1558, 1578-1617, 1623-1632, 1652-1691, 1697-1706, 1738-1777, 1783-1793, 1819-1845, 1851-1862, 1889-1921, 1927-1941, 1956-1988, 1994-2007, 2020-2046, 2052-2061, 2086-2113, 2119-2129, 2169-2195, 2201-2211, 2241-2271, 2277-2290, 2309-2339, 2345-2357, 2384-2410, 2416-2425, 2450-2489, 2495-2504, 2528-2567, 2573-2582, 2605-2644, 2650-2660, 2712-2748, 2754-2771, 2823-2859, 2865-2882, 2922-2958, 2964-2981, 2999-3038, 3044-3053, 3070-3096, 3102-3112, 3130-3156, 3162-3172, 3200-3233, 3239-3253, 3269-3308, 3314-3323, 3339-3378, 3384-3393, 3406-3432, 3438-3447, 3471-3502, 3508-3521, 3561-3587, 3593-3602, 3632-3671, 3677-3686, 3701-3740, 3746-3755, 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7174-7204, 7210-7222, 7243-7282, 7288-7298, 7316-7355, 7361-7370, 7387-7413, 7419-7429, 7458-7486, 7492-7503, 7531-7565, 7571-7584, 7623-7657, 7663-7677, 7709-7746, 7752-7768, 7794-7833, 7839-7848, 7864-7890, 7896-7905, 7920-7946, 7952-7961, 7978-8004, 8010-8020, 8062-8102, 8108-8118, 8161-8201, 8207-8217, 8259-8299, 8305-8315, 8357-8397, 8403-8413, 8455-8495, 8501-8511, 8567-8593, 8599-8610, 8625-8651, 8657-8668, 8683-8709, 8715-8724, 8741-8767, 8773-8783, 8804-8830, 8836-8845, 8869-8908, 8914-8923, 8947-8986, 8992-9001, 9026-9056, 9062-9076, 9104-9151, 9157-9181, 9203-9231, 9237-9248, 9271-9300, 9306-9318, 9344-9376, 9382-9396, 9417-9445, 9451-9462, 9485-9514, 9520-9533, 9561-9593, 9599-9613, 9660-9688, 9694-9705, 9752-9784, 9790-9804, 9819-9845, 9851-9860, 9877-9903, 9909-9919, 9939-9978, 9984-9993, 10013-10052, 10058-10068, 10093-10111, 10117-10127, 10151-10182, 10188-10201, 10226-10265, 10271-10280, 10304-10343, 10349-10358, 10375-10401, 10407-10417, 10441-10480, 10486-10496, 10518-10557, 10563-10573, 10623-10667, 10673-10685, 10731-10775, 10781-10793, 10838-10882, 10888-10900, 10946-10990, 10996-11008, 11024-11063, 11069-11079, 11093-11119, 11125-11135, 11151-11190, 11196-11206, 11228-11267, 11273-11283 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nccl_ops.py 124 103 17% 49-85, 91-104, 127-154, 160-170, 193-227, 233-249 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py 5462 5063 7% 49-95, 101-125, 153-199, 205-229, 259-306, 312-337, 366-413, 419-444, 479-517, 523-537, 580-619, 625-639, 669-681, 690-693, 702-715, 741-772, 778-790, 811-837, 843-853, 917-983, 989-1027, 1072-1142, 1148-1187, 1232-1302, 1308-1347, 1387-1436, 1442-1469, 1495-1541, 1547-1571, 1613-1687, 1693-1722, 1748-1794, 1800-1824, 1863-1916, 1922-1950, 1971-2008, 2014-2029, 2049-2086, 2092-2107, 2160-2228, 2234-2261, 2306-2388, 2394-2423, 2467-2549, 2555-2584, 2632-2674, 2680-2702, 2728-2772, 2778-2801, 2827-2870, 2876-2899, 2917-2956, 2962-2971, 2987-3013, 3019-3029, 3088-3148, 3154-3185, 3224-3262, 3268-3283, 3366-3426, 3432-3463, 3498-3537, 3543-3558, 3603-3655, 3661-3685, 3735-3782, 3788-3808, 3858-3905, 3911-3934, 3988-4036, 4042-4065, 4110-4162, 4168-4193, 4239-4291, 4297-4322, 4359-4398, 4404-4423, 4465-4512, 4518-4542, 4574-4601, 4607-4618, 4651-4677, 4683-4694, 4714-4753, 4759-4768, 4803-4861, 4867-4889, 4913-4958, 4964-4987, 5001-5030, 5036-5048, 5066-5096, 5102-5115, 5133-5159, 5165-5174, 5201-5247, 5253-5277, 5305-5351, 5357-5381, 5413-5460, 5466-5492, 5524-5572, 5578-5603, 5634-5681, 5687-5712, 5743-5791, 5797-5822, 5853-5889, 5895-5911, 5939-5991, 5997-6025, 6056-6091, 6097-6113, 6141-6193, 6199-6226, 6253-6288, 6294-6309, 6350-6406, 6412-6439, 6467-6497, 6503-6516, 6550-6594, 6600-6623, 6688-6742, 6748-6773, 6807-6840, 6846-6861, 6909-6964, 6970-7001, 7032-7098, 7104-7144, 7177-7251, 7257-7299, 7332-7403, 7409-7451, 7489-7545, 7551-7583, 7615-7682, 7688-7729, 7761-7831, 7837-7878, 7912-7989, 7995-8038, 8072-8148, 8154-8197, 8234-8317, 8323-8370, 8403-8474, 8480-8522, 8559-8642, 8648-8695, 8733-8789, 8795-8827, 8866-8925, 8931-8964, 9004-9076, 9082-9123, 9168-9245, 9251-9294, 9343-9396, 9402-9432, 9457-9513, 9519-9549, 9599-9653, 9659-9689, 9743-9804, 9810-9842, 9875-9934, 9940-9972, 10006-10050, 10056-10079, 10105-10140, 10146-10160, 10186-10221, 10227-10241, 10268-10304, 10310-10325, 10345-10384, 10390-10399, 10412-10438, 10444-10453, 10470-10496, 10502-10512, 10529-10555, 10561-10571, 10594-10633, 10639-10648, 10664-10690, 10696-10706, 10724-10750, 10756-10765, 10792-10821, 10827-10838, 10853-10892, 10898-10907, 10923-10949, 10955-10965, 10980-11019, 11025-11034, 11050-11076, 11082-11092, 11123-11153, 11159-11170, 11210-11242, 11248-11261, 11299-11330, 11336-11349 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_parsing_ops.py 1066 994 7% 52-103, 109-137, 164-209, 215-227, 252-291, 297-306, 326-361, 367-381, 400-434, 440-453, 520-582, 588-628, 708-782, 788-834, 925-1095, 1101-1211, 1314-1499, 1505-1632, 1689-1755, 1761-1800, 1886-2018, 2024-2118, 2138-2179, 2185-2195, 2211-2250, 2256-2265, 2289-2319, 2325-2337 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_array_ops.py 58 42 28% 76-119, 125-146 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_conversion_ops.py 189 163 14% 61-106, 112-130, 157-192, 198-214, 279-325, 331-355, 386-425, 431-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_ragged_math_ops.py 53 37 30% 63-95, 101-114 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_random_ops.py 511 460 10% 43-83, 89-109, 141-180, 186-204, 232-267, 273-289, 303-329, 335-345, 361-396, 402-418, 453-493, 499-519, 548-581, 587-602, 626-662, 668-684, 717-745, 751-767, 799-835, 841-858, 884-920, 926-942 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_resource_variable_ops.py 631 526 17% 38-57, 62-69, 87-106, 111-118, 144-155, 160-167, 189-208, 213-219, 247-264, 269-278, 330-356, 362-371, 389-422, 428-442, 467-479, 486-488, 497-507, 538-579, 585-603, 618-647, 653-664, 701-721, 726-734, 771-791, 796-804, 841-861, 866-874, 911-931, 936-944, 981-1001, 1006-1014, 1051-1071, 1076-1084, 1111-1131, 1136-1144, 1173-1203, 1209-1226, 1247-1265, 1271-1280, 1303-1332, 1338-1350 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_rnn_ops.py 350 306 13% 84-130, 136-157, 220-257, 263-275, 338-375, 381-393, 460-501, 507-525, 597-625, 631-641, 753-782, 788-798, 866-912, 918-938, 987-1022, 1028-1039 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_script_ops.py 136 104 24% 39-80, 86-104, 128-140, 144, 152, 154-157, 165-179, 194-226, 232-247 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sdca_ops.py 322 287 11% 37-76, 82-91, 170-329, 335-421, 497-617, 623-709, 730-751, 756 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sendrecv_ops.py 97 79 19% 42-91, 97-116, 138-173, 178-193 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_set_ops.py 187 160 14% 56-94, 100-116, 167-208, 214-232, 258-292, 298-312, 378-423, 429-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_sparse_ops.py 1272 1150 10% 68-108, 114-132, 169-209, 215-232, 298-327, 333-343, 409-439, 445-455, 484-519, 525-539, 559-594, 600-614, 663-695, 701-716, 750-781, 787-799, 870-924, 930-965, 1040-1103, 1109-1143, 1173-1200, 1206-1218, 1242-1269, 1275-1287, 1315-1342, 1348-1360, 1427-1456, 1462-1474, 1505-1533, 1539-1550, 1586-1621, 1627-1642, 1686-1723, 1729-1744, 1780-1815, 1821-1836, 1880-1917, 1923-1938, 1973-2002, 2008-2019, 2061-2089, 2095-2106, 2153-2181, 2187-2200, 2225-2256, 2262-2274, 2309-2336, 2342-2353, 2387-2418, 2424-2438, 2472-2503, 2509-2523, 2572-2607, 2613-2630, 2652-2680, 2686-2698, 2734-2773, 2779-2799, 2845-2882, 2888-2904, 2985-3025, 3031-3049 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_special_math_ops.py 172 145 16% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_spectral_ops.py 714 630 12% 33-59, 65-74, 87-113, 119-128, 141-167, 173-182, 195-221, 227-236, 249-275, 281-290, 303-329, 335-344, 364-403, 409-418, 438-477, 483-492, 512-551, 557-566, 586-625, 631-640, 660-699, 705-714, 734-773, 779-788, 819-849, 855-868, 900-930, 936-949, 981-1011, 1017-1030, 1058-1089, 1095-1108, 1137-1168, 1174-1187, 1216-1247, 1253-1266 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_state_ops.py 513 432 16% 46-69, 75, 96-114, 120, 141-159, 165, 181-197, 203, 226-242, 248, 263-277, 283, 300-329, 335-346, 403-427, 432-444, 501-525, 530-542, 602-627, 632-645, 689-709, 715, 756-776, 782, 825-845, 851, 894-914, 920, 961-981, 987, 1044-1064, 1070, 1129-1149, 1155, 1213-1233, 1239, 1282-1302, 1308, 1355-1375, 1381, 1413-1435, 1441, 1456-1482, 1488, 1512-1538, 1544 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateful_random_ops.py 353 314 11% 37-67, 73-85, 106-126, 131-139, 157-191, 197-214, 233-264, 270-284, 304-335, 341-356, 378-409, 415-430, 451-482, 488-502, 522-553, 559-574, 601-630, 636-649 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_stateless_random_ops.py 354 315 11% 40-75, 81-95, 123-155, 161-177, 200-228, 234-246, 268-299, 305-318, 343-373, 379-393, 416-447, 453-467, 489-521, 527-541, 565-593, 599-612, 636-667, 673-687 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_string_ops.py 1301 1175 10% 64-128, 134-159, 178-217, 223-232, 257-299, 305-317, 363-399, 405-421, 451-477, 483-493, 518-550, 556-570, 592-619, 625-635, 655-691, 697-712, 734-775, 781-800, 826-861, 867-884, 912-941, 947-959, 980-1022, 1028-1040, 1083-1137, 1143-1166, 1212-1243, 1249-1262, 1313-1343, 1349-1362, 1378-1417, 1423-1432, 1453-1481, 1487-1497, 1526-1569, 1575-1586, 1625-1675, 1681-1697, 1718-1760, 1766-1778, 1884-1913, 1919-1933, 1989-2043, 2049-2073, 2134-2190, 2196-2221, 2271-2313, 2319-2337, 2363-2402, 2408-2417, 2495-2570, 2576-2598, 2647-2699, 2705-2720 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_summary_ops.py 386 335 13% 33-52, 57-63, 80-104, 109-119, 136-160, 165-176, 189-208, 213-219, 233-252, 257-264, 278-312, 318-332, 350-376, 381-394, 409-429, 434-442, 458-478, 483-492, 510-536, 541-554, 569-589, 594-602, 618-638, 643-652, 669-689, 694-704 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_tpu_ops.py 3018 2798 7% 59-98, 104-118, 141-169, 175-185, 206-267, 273-300, 315-335, 340-346, 370-397, 403-413, 436-468, 473-489, 537-598, 603-647, 701-779, 784-841, 855-884, 890-900, 916-954, 960-978, 1002-1037, 1042-1062, 1079-1104, 1109-1119, 1144-1181, 1186-1208, 1237-1278, 1283-1304, 1335-1379, 1384-1406, 1435-1477, 1482-1503, 1534-1578, 1583-1605, 1632-1673, 1678-1698, 1727-1770, 1775-1796, 1827-1868, 1873-1895, 1924-1965, 1970-1991, 2022-2066, 2071-2093, 2124-2167, 2172-2194, 2221-2261, 2266-2286, 2315-2358, 2363-2384, 2411-2452, 2457-2477, 2506-2549, 2554-2575, 2604-2643, 2648-2669, 2700-2743, 2748-2770, 2795-2836, 2841-2860, 2880-2916, 2922-2935, 2958-3003, 3009-3031, 3044-3063, 3068-3074, 3089-3108, 3113-3119, 3138-3175, 3181-3200, 3221-3261, 3267-3288, 3311-3342, 3348-3358, 3388-3436, 3442-3463, 3494-3543, 3549-3570, 3600-3648, 3654-3675, 3706-3755, 3761-3782, 3811-3859, 3865-3886, 3916-3965, 3971-3992, 4023-4072, 4078-4099, 4129-4177, 4183-4204, 4235-4284, 4290-4311, 4342-4391, 4397-4418, 4447-4495, 4501-4522, 4552-4601, 4607-4628, 4657-4706, 4712-4733, 4763-4812, 4818-4839, 4869-4917, 4923-4944, 4975-5024, 5030-5051, 5073-5121, 5127-5148, 5175-5207, 5212-5231, 5245-5264, 5269-5275, 5291-5317, 5323-5331, 5359-5393, 5399-5412, 5428-5454, 5460-5468, 5486-5525, 5531-5550, 5580-5663, 5668-5722, 5749-5793, 5799-5820, 5844-5872, 5878-5888, 5905-5931, 5937-5946 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_user_ops.py 43 28 35% 32-57, 63-71 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker.py 145 117 19% 39-45, 49-54, 83-132, 160-193, 202-208, 221-242, 254-268, 321-335, 339-345, 393-395 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradient_checker_v2.py 140 116 17% 37-43, 59-64, 78-93, 110-127, 150-197, 221-261, 266-281, 287-293, 332-335, 351-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_impl.py 59 25 58% 168-169, 298-299, 342-357, 392-424, 433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gradients_util.py 446 380 15% 61-69, 97-133, 137, 162-227, 231-234, 250-254, 279-289, 295-299, 303, 308-318, 323-355, 362-374, 383, 388-392, 405-411, 428, 444-457, 471-476, 490-716, 721-728, 734-766, 771-781, 786-805, 810-813, 817-821, 826-838, 846-867, 932-987 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/histogram_ops.py 28 13 54% 77-100, 146-149 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_grad.py 91 69 24% 39-50, 64-69, 84-91, 105-113, 131-156, 167, 188-381 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops.py 51 32 37% 195-203, 228-239, 245-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/image_ops_impl.py 1010 802 21% 75-81, 93, 108-113, 133-150, 171, 193, 212-236, 258, 277-301, 315-320, 360, 401, 422-447, 481, 515, 537-546, 581-594, 610-625, 641-657, 706-715, 772-847, 907-956, 990-1036, 1070-1155, 1183-1255, 1315-1333, 1470-1511, 1523-1590, 1628-1631, 1668-1671, 1698-1717, 1748-1752, 1784-1792, 1830-1843, 1885-1899, 1945-1963, 2012-2062, 2086-2097, 2122-2132, 2168-2175, 2230-2241, 2277-2289, 2327-2339, 2377-2385, 2426-2437, 2455-2457, 2471-2473, 2540, 2586-2656, 2694-2728, 2823-2824, 2930-2931, 2988-2992, 3064-3079, 3129-3133, 3175-3179, 3211-3215, 3238-3242, 3278-3282, 3305-3309, 3329-3354, 3393-3408, 3444-3466, 3471-3483, 3519-3565, 3627-3640, 3690-3771, 3826-3844, 3861-3885, 3893, 3906, 3919, 4036, 4053-4055, 4137, 4222, 4295-4300, 4354-4356, 4408, 4427 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops.py 509 330 35% 68, 76, 97, 113, 117, 132-134, 137, 215-222, 225-229, 237, 260-263, 266-268, 272, 300-303, 306-308, 312, 346-349, 352-354, 358, 407-409, 412-427, 431, 480, 482, 487, 495-518, 522, 563-565, 568-595, 598, 626-628, 631-663, 666, 688-690, 693, 696, 708-715, 726-733, 758-771, 785, 803-813, 828-845, 860-877, 903-913, 926, 939-944, 959-973, 988-1002, 1028-1041, 1056, 1075-1093, 1109-1129, 1144-1165, 1185-1186, 1189-1201, 1204, 1236, 1265, 1269, 1318, 1345, 1370, 1394, 1410-1425, 1443 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/init_ops_v2.py 241 105 56% 59, 67, 88-89, 168-171, 239-243, 257-259, 263, 300-303, 316-319, 323, 364-367, 380-381, 385, 429-432, 445-446, 450, 509, 511, 515, 518, 541, 544, 546, 551-552, 554-555, 561, 612-614, 628-650, 653, 684, 698-708, 711, 757, 796, 803, 864, 907, 947, 987, 1004, 1006, 1013-1017, 1037, 1048, 1054-1058, 1064, 1072-1076 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/initializers_ns.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/inplace_ops.py 41 16 61% 53-64, 90, 116, 142, 160-161, 191, 221, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/io_ops.py 120 54 55% 68-71, 93-94, 131-137, 142, 161-171, 193-206, 224-228, 240-244, 259-262, 278-282, 287, 298-301, 337-338, 369-371, 410-417, 446-451, 479-481, 511-512 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/adjoint_registrations.py 50 18 64% 37, 48, 54, 60-64, 76-80, 92, 106, 118-123, 134 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/cholesky_registrations.py 31 6 81% 36, 46, 57, 70, 83, 96 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/inverse_registrations.py 68 31 54% 40, 51, 57, 68, 74, 87, 132-185, 200, 213, 225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg.py 36 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linalg_impl.py 400 323 19% 95-97, 126-128, 135-145, 150-161, 166-178, 183-203, 208-229, 262-339, 439-490, 496-538, 591-618, 623-635, 657-673, 746-802, 848-896, 949-959, 1000-1036, 1041-1068, 1073-1094 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator.py 387 259 33% 188-213, 218-222, 227, 232, 238, 242, 246, 250, 257-264, 269, 282, 288, 303-308, 322, 338-339, 344-349, 365-366, 381-382, 387-391, 404-407, 424-425, 430-435, 448-451, 468-469, 474-479, 483-493, 500-508, 527-528, 532-544, 560-561, 564-568, 586-587, 591-592, 598, 629-653, 656, 659-661, 690-696, 699-704, 718-723, 726-733, 747-752, 756-763, 768-771, 814-846, 850-852, 893-900, 914-917, 937-945, 966-970, 974-986, 990-991, 995, 1022-1023, 1026, 1039-1040, 1044, 1056-1059, 1062, 1078-1081, 1084-1093, 1105-1106, 1109, 1122-1127, 1137, 1142, 1155, 1161, 1166-1169, 1174-1176, 1190-1202, 1212-1215, 1220 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_addition.py 160 105 34% 99-138, 144-149, 168-187, 195-211, 217-223, 233-235, 253, 258, 264, 280-290, 301-302, 307-317, 330-331, 334, 346-347, 350-355, 367, 371-375, 412-424 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_adjoint.py 72 39 46% 116-155, 160, 163, 166, 169, 173-174, 178-179, 183, 187, 190-192, 195, 198-200, 203, 207, 210-212, 215, 218-221, 224 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_algebra.py 112 49 56% 37-48, 53, 58, 63, 68, 73, 90-96, 113-119, 138-144, 163-169, 186-192, 228, 231, 270, 273, 315, 318, 361, 364, 404, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_diag.py 153 119 22% 156-215, 219, 223-238, 242-260, 263-273, 276-279, 282-285, 288-299, 302-308, 311-314, 317-341, 344, 348, 352, 356-362, 378-386 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_block_lower_triangular.py 191 152 20% 224-241, 250-252, 259-283, 290-301, 304-310, 314-318, 324, 328-344, 348-367, 370-417, 420-425, 428-431, 460-510, 513-520, 523-526, 529-550, 553, 557-563, 579-587 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_circulant.py 204 144 29% 93-126, 130-138, 171, 177, 180-186, 190, 194, 210-224, 239-260, 272-273, 285-286, 300-302, 305-321, 324-331, 345-350, 356, 366-368, 373, 381-402, 405-425, 428-430, 433-436, 439-450, 455-478, 490-512, 747, 758, 927, 1077, 1089-1095 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_composition.py 86 62 28% 147-187, 191, 195-211, 215-233, 239-248, 251-254, 257-260, 270-279 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_diag.py 83 49 41% 143-168, 172-173, 178-179, 182-184, 188, 191, 196-205, 210, 217-220, 223-224, 227, 230-234, 237-240, 243, 246, 249-251, 254, 257-258 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_full_matrix.py 38 17 55% 137-151, 155-172, 177, 180, 183, 187, 190 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_householder.py 86 52 40% 126-156, 160-162, 168-169, 172-174, 177, 180, 185, 200-207, 211-212, 219, 223, 228, 231-236, 240-243, 247-254, 258, 262 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_identity.py 234 171 27% 48-74, 79-83, 87, 91-98, 255-292, 295-301, 304-308, 311, 314, 317, 322-350, 354-358, 361, 364, 367, 371-380, 384, 396-400, 403, 406, 411-437, 442-470, 599-630, 634-638, 641-644, 647, 651, 656-657, 664-668, 671-675, 678, 681, 685-689, 693-702, 706, 718-731, 734, 739, 747 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_inversion.py 52 27 48% 117-168, 173, 176, 179, 182, 185, 188, 191, 194, 197, 200, 203, 206 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_kronecker.py 205 169 18% 38, 46, 55-62, 171-231, 235, 239-257, 260-277, 317-387, 399-405, 409-415, 419-422, 433-504, 507-522, 525-544, 550-563, 566-570, 575-579 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_low_rank_update.py 148 108 27% 185-263, 268-281, 286-294, 300, 305, 310, 315, 320, 325, 328-331, 334-340, 344-360, 363-374, 380-394, 397-435, 442-448 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_lower_triangular.py 49 22 55% 141-159, 164-165, 171, 175, 178, 181, 184, 189, 193, 196, 200-201, 205, 208 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_permutation.py 81 48 41% 143-156, 166-178, 183-184, 187-189, 192, 195-196, 199-220, 226, 231, 234-235, 240-241, 247, 251 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_toeplitz.py 82 50 39% 142-159, 163-170, 176-178, 181-187, 190, 210-224, 229, 234-235, 239-263, 267, 271, 275-278 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_tridiag.py 122 87 29% 175-190, 199-213, 216-230, 236-266, 270-287, 290-294, 299-331, 334-341, 344-359, 369, 373 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_util.py 150 122 19% 105-116, 122-125, 130-135, 151, 160-161, 182-187, 201-209, 228-236, 241-243, 251-255, 311-354, 359-373, 383-467, 491-502 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/linear_operator_zeros.py 157 118 25% 179-232, 235-241, 244-248, 251, 256, 261, 266-294, 297-320, 323-326, 330-333, 336, 348, 353-399, 404-432, 437-441, 445, 449-456, 459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/matmul_registrations.py 65 28 57% 37-50, 65-66, 73-74, 82, 102-105, 112-115, 125, 142, 159, 175, 191, 209 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/registrations_util.py 29 21 28% 27-44, 49-62, 71-78, 86-91 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/solve_registrations.py 55 22 60% 37-50, 67, 75-76, 83-84, 92, 112, 129, 146, 162, 181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/conjugate_gradient.py 52 37 29% 76-136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/gen_sparse_csr_matrix_ops.py 624 562 10% 48-77, 83-95, 109-137, 143-154, 177-207, 213-224, 239-266, 272-283, 303-329, 335-347, 399-459, 465-495, 518-544, 550-560, 573-599, 605-614, 677-703, 709-719, 739-766, 772-782, 797-825, 831-843, 937-965, 971-983, 1083-1133, 1139-1164, 1183-1215, 1221-1234, 1248-1275, 1281-1291, 1307-1335, 1341-1353 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_grad.py 127 101 20% 30-34, 40-41, 65-66, 73, 80-81, 95-169, 175-222, 231-233 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg/sparse/sparse_csr_matrix_ops.py 157 96 39% 56, 61-69, 74, 80-87, 105-116, 124-144, 181-241, 253, 257, 261, 265, 269, 273, 277, 281, 286, 289, 294, 301, 307, 310, 330-352, 357, 360-370, 373, 376-377 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_grad.py 389 345 11% 53-54, 61-366, 372-378, 392-443, 449-454, 462-480, 486-510, 516-524, 537-601, 609-633, 639-675, 690-811, 816-819, 824-827, 833-848, 854-867, 881-898, 917-937 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops.py 166 121 27% 65-79, 139-140, 181-186, 227, 293-367, 393-398, 419-424, 446-447, 469-470, 535-540, 607, 682-761 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/linalg_ops_impl.py 35 24 31% 42-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/list_ops.py 141 87 38% 49-52, 60, 68, 76, 85, 97, 110-114, 127, 139, 148, 161-170, 176, 184-189, 194, 201-209, 214-217, 227-243, 249-259, 265-274, 279-281, 287-294, 301-311, 317-325, 350-371 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/logging_ops.py 147 89 39% 48, 112, 120-124, 129, 258-375, 383, 387-390, 426-429, 487-491, 542-552, 583-586, 606-610, 624-634, 667-670 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/lookup_ops.py 638 452 29% 63, 81-84, 99-103, 117-119, 122, 127, 132, 137, 141, 145, 165-179, 182, 187, 198-199, 217-236, 243, 279-291, 294-303, 307, 319-325, 364, 372, 385-386, 391, 396, 400, 405-411, 428-445, 461-467, 587-626, 641-654, 658-669, 712, 765, 807-814, 818-820, 896-933, 936-938, 941-944, 948-951, 956, 960-962, 966, 970-975, 979-988, 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189-190, 204, 217, 231-235, 264-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_grad.py 12 4 67% 28-31 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/manip_ops.py 12 1 92% 30 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/map_fn.py 85 63 26% 149-287, 417 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/math_grad.py 1177 911 23% 37, 42-43, 48-49, 56-64, 91-136, 144, 152-214, 219-235, 241, 246, 252-266, 277-315, 321, 327-334, 340-341, 349-350, 358-359, 366-367, 374-375, 382-383, 389-399, 405, 411, 426-445, 452-464, 470, 476, 482, 504-530, 535-536, 542, 548-549, 555-556, 561-566, 571-576, 581-586, 591-592, 597-605, 611-612, 618-625, 631-637, 643-650, 656-662, 668-674, 680-690, 697-707, 714-724, 731-734, 740-743, 749-752, 758-761, 767-773, 779-785, 790-793, 799-803, 809-814, 820-822, 829-831, 838-844, 850-857, 863-866, 872-874, 880-882, 888-890, 896-901, 907-914, 920-938, 944-963, 970-971, 978-1004, 1014-1032, 1040-1058, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/control_flow_ops.py 165 134 19% 64-106, 111-114, 122-132, 182-201, 206-219, 224-317, 398-407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/gradients.py 57 45 21% 48-80, 112-147 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parallel_for/pfor.py 1990 1462 27% 75-80, 103-111, 126-225, 229, 234, 239-242, 247, 252, 257, 261-265, 269, 274, 297-340, 344-354, 369-396, 400-464, 474-483, 487-514, 520-559, 604-679, 694-696, 705-711, 721-742, 746, 750, 753-754, 757-768, 771-781, 785, 789, 792, 796, 799-800, 906, 915-919, 946-953, 957-982, 990-993, 997, 1001, 1022-1060, 1075, 1088, 1101, 1105-1106, 1167-1184, 1188-1192, 1224-1239, 1243-1257, 1262-1266, 1279-1292, 1296-1297, 1300-1302, 1306-1329, 1332-1516, 1521, 1526, 1530, 1534, 1544, 1556-1558, 1563-1565, 1570-1572, 1580-1582, 1587-1589, 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3455-3459, 3464-3485, 3492-3503, 3511-3520, 3532-3548, 3553-3580, 3587-3610, 3616-3628, 3632-3638, 3642-3662, 3668-3715, 3728, 3738-3741 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_config.py 305 240 21% 207-228, 303, 323, 358, 412-434, 454-463, 467, 471, 475, 491-516, 520-531, 535-537, 541-548, 553-568, 572-589, 593-603, 607-619, 623-627, 632-665, 704-758, 778-803, 827-885, 893-901 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/parsing_ops.py 165 110 33% 62-76, 304-313, 318, 339-370, 406, 442-447, 545-567, 597-689, 779-801, 830-833, 867-875, 907-918, 965, 1013-1020, 1033-1047 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/partitioned_variables.py 68 48 29% 104-154, 181-218, 233-237, 288-311 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/proto_ops.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/__init__.py 24 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_array_ops.py 187 158 16% 92-201, 230-243, 273-305, 332-373, 431-448, 474-477, 502-506, 520-528, 574-634, 660-689 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_ops.py 46 34 26% 64-124 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py 61 44 28% 69-141, 147-183 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_concat_ops.py 110 87 21% 67-70, 115-118, 136-202, 219-238, 253-289, 295-297, 302-310, 315-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_config.py 6 1 83% 33 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_conversion_ops.py 58 39 33% 36-39, 49-52, 57, 64-106, 113-137, 141, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_dispatch.py 243 116 52% 82, 90-102, 114, 119-160, 175, 182-231, 246, 252-253, 258-261, 264-284, 417, 427, 435-437, 441, 445-447, 452-456, 515, 548, 550, 559-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_factory_ops.py 121 99 18% 78-84, 133-143, 168-240, 260-271, 277-310, 336-346 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_functional_ops.py 39 25 36% 70-91, 112-128 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_gather_ops.py 98 80 18% 88-118, 167-261, 270-295 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_getitem.py 161 136 16% 97-103, 124-187, 200-226, 244-340, 360-361, 381-389, 394-406, 437-456, 462-471 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_map_ops.py 132 103 22% 169-331, 360-364, 372-386, 395-403, 409-418, 423-427, 434-459 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_math_ops.py 172 110 36% 88-105, 112-114, 193-256, 262, 271, 280, 289, 298-308, 313-324, 469-542, 551, 561, 572, 583, 594-608, 612, 618-619, 626-627 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_operators.py 44 1 98% 74 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_squeeze_op.py 53 40 25% 52-122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_string_ops.py 192 153 20% 59-78, 121-175, 219-220, 280-281, 322-325, 385-394, 400-452, 493-512, 563-575, 626-643, 648, 721-803 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_tensor.py 823 674 18% 268-311, 363-428, 472-497, 531-553, 587-608, 637-659, 718-795, 837-858, 888-897, 927-936, 959-983, 992, 1011-1020, 1031-1032, 1056, 1079, 1105, 1131-1134, 1161-1166, 1192-1196, 1227-1233, 1255-1266, 1290-1291, 1315-1316, 1345-1360, 1377-1383, 1410-1435, 1456-1466, 1491-1494, 1509-1533, 1570-1583, 1652-1793, 1828-1839, 1878-1905, 1927-1930, 1985-2001, 2030-2031, 2038-2041, 2075-2086, 2096-2099, 2105-2108, 2112-2117, 2140-2143, 2151, 2158, 2162, 2167, 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170-179, 206-212, 232-244, 247-260, 269, 274, 279, 282-309, 328-340, 344, 385, 420-436, 440, 444, 448-462, 466-471, 474-480, 526-544, 548, 552, 556-582, 586-603, 606-614, 633-637, 697-719, 723, 728, 732-746, 762-794, 797-805, 896-941, 945, 949, 953-993, 1019-1071, 1074-1089, 1100-1104, 1123, 1147, 1151-1158, 1162-1168, 1179, 1190, 1200, 1234-1255, 1261-1264, 1268, 1271-1277, 1281-1287, 1291-1301, 1305-1327, 1345-1346, 1350-1354 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_cell_wrapper_impl.py 224 164 27% 109-173, 179-183, 187, 191, 195, 198-199, 202-203, 209-215, 225-250, 271-289, 293-308, 312-319, 337-338, 342, 346, 349-350, 369-381, 385-396, 400-407, 424-425, 429, 433, 436-438, 442-443, 446-448, 453-465, 471-494, 498-504, 508-515 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/rnn_grad.py 14 5 64% 26-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/script_ops.py 174 76 56% 59-67, 83-85, 102-119, 124-150, 171-172, 183, 203-212, 230-253, 257, 282, 295, 319, 336, 347, 357-364, 452-457, 517-524, 536, 555 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sdca_ops.py 10 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/session_ops.py 128 85 34% 38, 56-60, 63-64, 67, 71-76, 85, 90, 94-99, 103-108, 117-118, 123-124, 129-130, 135, 173-178, 214-219, 239-243, 247, 251, 256-267, 272-288, 293-302 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sets_impl.py 55 31 44% 51-57, 81-90, 118-133, 200-201, 277-280, 356-357 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/dct_ops.py 77 60 22% 33-47, 97-179, 223-225 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/fft_ops.py 217 146 33% 35-42, 48-60, 65-108, 116-140, 150-170, 198, 203-204, 209-212, 217-218, 223-226, 231-232, 237-240, 250-320, 336-360, 393-403, 434-444 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mel_ops.py 57 39 32% 46-48, 64-66, 73-89, 161-218 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/mfcc_ops.py 20 9 55% 89-108 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/reconstruction_ops.py 60 48 20% 54-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/shape_ops.py 82 68 17% 33-54, 107-214 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/signal.py 29 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/spectral_ops.py 131 103 21% 70-94, 120-155, 224-276, 281-285, 331-365, 426-449 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/util_ops.py 33 20 39% 47-73 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/signal/window_ops.py 77 52 32% 47-51, 70-90, 110-117, 135-141, 165, 192, 217-239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sort_ops.py 53 33 38% 65-66, 107-109, 128-140, 155-204, 209-211 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_grad.py 130 88 32% 50-60, 83-96, 101-103, 109-115, 136-145, 166-201, 206, 212-241, 247, 253, 273-286, 291, 297, 306-312, 317-322 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/sparse_ops.py 516 356 31% 66-70, 87-91, 95-101, 118-124, 147-179, 202-211, 324-331, 336-366, 430-434, 490-517, 550, 595, 614-655, 678-680, 720-731, 784-830, 840, 889-914, 955, 995-1008, 1062, 1129-1144, 1211-1216, 1259-1269, 1319-1333, 1387-1392, 1435-1445, 1490-1494, 1552-1564, 1663, 1672-1718, 1751-1765, 1829-1871, 1925-1935, 1958, 1977-1979, 2013, 2041-2043, 2107-2114, 2179-2187, 2401-2419, 2476-2480, 2508-2519, 2546-2557, 2595-2619, 2643-2645, 2683-2685, 2756-2777, 2795-2806 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/special_math_ops.py 357 299 16% 87-101, 129-130, 157-158, 186-187, 214-215, 242-243, 266-267, 290-291, 296-303, 319-324, 404, 409-468, 490-551, 580-679, 684-687, 693-700, 707-714, 723-726, 731-802, 807-853, 861-869, 881-973 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/standard_ops.py 83 1 99% 115 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_grad.py 17 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/state_ops.py 131 81 38% 44-52, 74, 101-114, 130-133, 161-164, 192-195, 224-228, 249-251, 301-304, 363-366, 415-418, 478-481, 532-535, 596-599, 648-651, 700-703, 755-758, 810-813, 872-914 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/stateful_random_ops.py 279 177 37% 90, 95-97, 110-136, 140-145, 149-151, 155, 168-182, 206-207, 212-216, 220-223, 230-231, 235, 239, 243-250, 254, 257, 270-278, 369-387, 410, 439-444, 467-473, 500-507, 517-519, 529-530, 541-548, 552, 557, 562, 565, 585-589, 600, 621-626, 629, 657-664, 667, 705-721, 737-741, 789-794, 810, 832-840, 880-890, 913-916, 939 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/build_info.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/device_context.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/flags.py 59 18 69% 52-54, 56, 78, 81-86, 89, 92, 95, 98, 101, 104, 107, 110, 113 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/gfile.py 29 1 97% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/googletest.py 103 67 35% 56, 61-66, 72-94, 107, 112, 134-135, 143, 147, 150, 154-155, 184-218, 229-232, 251-258, 271-273 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/remote_utils.py 6 1 83% 22 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/resource_loader.py 47 22 53% 47-48, 60, 72-100, 121-125, 136 /usr/local/lib/python3.8/dist-packages/tensorflow/python/platform/self_check.py 22 12 45% 26-27, 44-53 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/profiler/traceme.py 27 16 41% 35-41, 44-45, 48-49, 53-60 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_mlir.py 14 4 71% 27, 34, 41, 47 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow.py 30 6 80% 38, 51, 61, 64-69 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py 65 39 40% 19-21, 31, 35-36, 40-55, 59, 63-71, 74, 78-82, 87-90 /usr/local/lib/python3.8/dist-packages/tensorflow/python/pywrap_tfe.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/builder_impl.py 231 176 24% 93-112, 122-131, 145-153, 168-182, 201-214, 220-228, 267-300, 345-393, 412-428, 437, 442-446, 456-465, 478-492, 507-511, 525-555, 570-615, 638-667, 688-706, 710-716, 731-740, 752-754, 759-774, 786-792, 796-797 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/constants.py 32 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_deserialization.py 202 39 81% 74, 80-86, 94-95, 104-105, 110, 112, 114, 116, 118, 121, 132, 190, 243-255, 364-365, 376, 380, 387-392, 398-403, 441, 461 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/function_serialization.py 71 56 21% 31-49, 54-75, 81-83, 92-103, 122-161 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py 270 68 75% 55-64, 93, 98, 128-130, 205, 212-219, 231, 234-241, 256, 263, 273-279, 308, 326-333, 342-346, 369, 391-394, 409, 435-442, 445, 453-454, 457, 460, 464, 468-470, 476-482, 586, 595, 612-614 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load_v1_in_v2.py 137 105 23% 55-57, 60, 64, 72-83, 89-92, 95-102, 109-126, 133-184, 188-257, 262-263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py 157 93 41% 63-67, 100-110, 135-161, 182-190, 194, 199-204, 208, 215-219, 244-246, 265, 298-299, 312-314, 319, 324, 329, 343-358, 379-381, 401-409, 425-433, 450-455 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/main_op_impl.py 23 7 70% 43-46, 70-72 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/method_name_updater.py 42 26 38% 71-72, 92-113, 130-148 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/__init__.py 12 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_output.py 155 93 40% 53, 57-63, 86-99, 134-148, 152, 156, 159-166, 182-185, 189, 192-199, 223, 228, 231, 269-278, 294-298, 301-303, 325-361, 365, 369, 373, 378, 381-382, 395, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/export_utils.py 115 84 27% 93-140, 154-182, 211-226, 243-247, 277-290, 309-325, 342-355 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/model_utils/mode_keys.py 43 5 88% 87, 100, 103, 106, 109 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/nested_structure_coder.py 250 95 62% 70, 81, 96, 107-111, 132, 135-139, 152, 164-166, 175, 178-182, 198, 201-205, 225, 228-236, 256, 259-262, 268-269, 279, 282-285, 291-292, 306, 309-312, 329, 332-335, 352, 355-358, 375, 378-382, 399, 402-405, 422, 425-431, 482, 486-494, 497, 501-511 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/revived_types.py 70 21 70% 78, 92, 94, 96, 98-99, 122, 128, 133, 142-147, 171, 175-181 /usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/save.py 392 316 19% 88-99, 103, 108-133, 136, 140-145, 171-210, 214, 218-234, 254-335, 339, 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100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer.py 128 95 26% 69-82, 90-96, 100, 110-112, 120-121, 132-137, 145-153, 160-163, 166-168, 193-204, 207-232, 244-254, 264-269, 285-294, 298-300 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/event_file_writer_v2.py 44 27 39% 74-96, 100, 110-112, 120-122, 131, 138-141 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer.py 133 90 32% 79-99, 119-142, 155-156, 159-161, 179-214, 217-227, 244-249, 263-273, 276-279, 360-372, 376, 380, 384, 387-388, 393-394, 402-403, 413-414, 421-422, 432-433 /usr/local/lib/python3.8/dist-packages/tensorflow/python/summary/writer/writer_cache.py 24 8 67% 43-48, 60-64 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tf2.py 14 1 93% 40 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tools/module_util.py 31 12 61% 24, 47, 50-56, 60-61, 63 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/__init__.py 4 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/api.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/bfloat16.py 26 13 50% 49-68, 78-80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/client/client.py 162 118 27% 34-35, 46, 50-54, 59-68, 75-81, 85-87, 106-141, 157-176, 181, 186-191, 196-200, 203, 206, 213-216, 220, 224, 228, 232, 236, 240, 244, 248-258, 270-281, 286-315 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/device_assignment.py 180 148 18% 36-56, 81-102, 108, 113, 118, 129, 133, 148-151, 157-158, 162-163, 167-168, 175, 198-213, 257-413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/feature_column.py 220 158 28% 106-155, 219-285, 295-304, 310, 314, 318, 322, 330, 334, 338, 341, 344, 347, 351, 373, 397-402, 405, 409, 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196-212 /usr/local/lib/python3.8/dist-packages/tensorflow/python/tpu/training_loop.py 82 70 15% 56-177, 201-222 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adadelta.py 46 28 39% 59-67, 70-72, 75-81, 84-86, 97-99, 110-112, 124-126 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad.py 48 26 46% 63-70, 73-80, 84-90, 93-94, 98-99, 107-108, 116-117, 126-127 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adagrad_da.py 59 39 34% 75-88, 91-100, 104-109, 112-116, 128-132, 144-148, 161-165 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/adam.py 93 67 28% 101-111, 114-119, 127-136, 139-147, 150-153, 167-170, 184-207, 210, 221-223, 226, 231-238 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_loops.py 21 13 38% 51-65 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/basic_session_run_hooks.py 491 361 26% 61, 65, 69, 83, 87, 99-108, 111-112, 125-139, 142-152, 155, 162-163, 166-167, 170, 212-231, 234-237, 243-247, 250-263, 266-270, 273-275, 306-315, 352-360, 363-366, 369-371, 374-377, 382-386, 412-417, 420-422, 425-427, 430, 433-442, 500, 503, 506, 509, 546-557, 560, 563-569, 572-588, 591, 594-602, 605-609, 613-634, 637-656, 669-678, 681, 684-690, 693, 696-702, 705-736, 743, 760-761, 764, 767-775, 809-817, 822-827, 831-839, 842-861, 864-865, 873-884, 903, 906-909, 913-933, 949-951, 955, 958-977, 991, 994, 1033-1037, 1041-1044, 1047-1055, 1058-1072, 1075-1078, 1085-1104 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py 266 201 24% 45-47, 61-63, 96-128, 167, 213-247, 270-305, 322-324, 351-364, 381-388, 410, 438-456, 476-484, 489-490, 506-508, 614-664, 668, 672, 686, 698, 702-717, 721-722, 743, 748, 771-824, 844-852 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_ops.py 76 60 21% 123-203, 332-416, 467-473 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale.py 158 90 43% 83, 88, 125, 141-162, 167-176, 180-188, 193, 198, 202, 228-239, 242, 245-246, 249, 252, 257-260, 275-277, 282, 324-335, 340, 344, 348, 351, 355-400, 403-409, 414, 424, 426, 428, 432 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scale_optimizer.py 79 52 34% 61-74, 78, 114-126, 129-136, 139-141, 147-150, 174-184, 211-221, 226-229, 233, 237, 241, 245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/loss_scaling_gradient_tape.py 73 53 27% 43, 115-125, 163-181, 190, 200, 238-320 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision.py 51 30 41% 33-74, 219, 332, 339-362, 382-386, 413 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/experimental/mixed_precision_global_state.py 7 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/ftrl.py 72 53 26% 95-130, 134-138, 141-150, 154-170, 186-202, 218-235, 252-267 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/gen_training_ops.py 1619 1436 11% 57-77, 83, 115-135, 141, 167-191, 197, 226-248, 254, 282-306, 312, 353-378, 384, 414-434, 440, 490-510, 516, 552-572, 578, 617-637, 643, 662-681, 687, 720-744, 750, 780-800, 806, 835-854, 860, 886-906, 912, 952-972, 978, 1011-1038, 1043-1056, 1086-1113, 1118-1131, 1156-1185, 1190-1206, 1236-1266, 1271-1286, 1313-1343, 1348-1365, 1404-1437, 1442-1459, 1498-1527, 1532-1547, 1575-1601, 1606-1618, 1665-1692, 1697-1712, 1747-1773, 1778-1791, 1829-1856, 1861-1874, 1893-1916, 1921-1933, 1965-1995, 2000-2017, 2049-2078, 2083-2099, 2127-2153, 2158-2171, 2199-2225, 2230-2243, 2269-2296, 2301-2313, 2351-2377, 2382-2395, 2423-2451, 2456-2472, 2500-2530, 2535-2553, 2585-2616, 2621-2638, 2668-2699, 2704-2722, 2769-2800, 2805-2822, 2860-2887, 2892-2908, 2949-2977, 2982-2998, 3034-3067, 3072-3090, 3126-3156, 3161-3179, 3211-3239, 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/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/momentum.py 40 22 45% 80-83, 86-87, 90-98, 101-102, 111-112, 121-122, 131-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/monitored_session.py 481 348 28% 153-188, 192-247, 251, 255, 259, 263, 267, 271, 275, 279, 283, 288-300, 315, 337-427, 512-601, 614, 639-644, 648-657, 660-661, 690-694, 698-707, 710-711, 734-751, 756-758, 774, 819-834, 852-853, 857, 861, 871, 874, 877, 880, 883-887, 893-897, 902-910, 915-929, 939, 950, 1034, 1118-1124, 1132, 1154-1155, 1159, 1163, 1173-1177, 1185, 1188-1197, 1200, 1206-1207, 1230-1231, 1235-1238, 1249-1272, 1276-1295, 1299-1316, 1342-1344, 1349-1351, 1354-1365, 1368-1384, 1412-1414, 1418, 1422-1453, 1458-1481, 1484-1486, 1501-1514 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/moving_averages.py 131 103 21% 87-114, 152-178, 220-266, 378-382, 387, 421-473, 485, 509-511, 545-561 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/optimizer.py 403 311 23% 58-61, 76-80, 85-87, 97, 102, 109, 112, 115, 118-132, 139, 142, 146-151, 158, 161, 165-176, 188, 191, 194, 199-213, 327-343, 353, 399-412, 458-519, 523-529, 561-640, 667-735, 755-773, 783, 794-811, 816-843, 848-857, 862-866, 869-875, 883, 894-898, 914, 924, 932, 944, 957, 980-982, 1002, 1032-1038, 1057, 1074, 1090-1094, 1109-1116, 1134-1142, 1156-1163, 1171-1179, 1202-1239, 1245 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_adagrad.py 44 25 43% 63-74, 77-82, 85-90, 95-96, 103-104, 111-112, 120-121 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/proximal_gradient_descent.py 30 13 57% 57-62, 65, 74, 83, 93, 103-107 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/py_checkpoint_reader.py 40 17 58% 31-48, 61, 73-74, 98-99 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/quantize_training.py 15 4 73% 45-50 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/queue_runner_impl.py 176 128 27% 97-118, 138-165, 175-192, 196, 200, 204, 208, 212, 231, 236, 248-283, 293-298, 327-356, 368-385, 390, 411, 451-480 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/rmsprop.py 70 50 29% 107-118, 121-131, 134-142, 145-161, 173-189, 201-218, 231-248 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saver.py 480 383 20% 84, 104-124, 144-154, 173-179, 194, 206-207, 254-285, 298-311, 335-361, 379-390, 409-417, 462, 484-557, 574-583, 598-610, 800-843, 846-848, 851, 856-902, 906-914, 926-927, 931-942, 957-973, 981, 992-1008, 1021, 1032, 1045-1049, 1061-1062, 1075-1080, 1139-1217, 1253, 1283-1334, 1346, 1460, 1471-1490, 1496-1514, 1580-1599, 1603-1607, 1626-1634, 1679-1728 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/functional_saver.py 119 48 60% 51, 64-73, 118, 148, 153-154, 165-169, 179-182, 188-191, 202-263, 284 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_hook.py 16 4 75% 44, 51, 55, 59 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object.py 34 8 76% 41, 47-51, 55, 78, 101 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/saving/saveable_object_util.py 161 98 39% 54-56, 63-64, 67-70, 84-85, 90-95, 101, 111, 120, 139, 143, 145-169, 175-183, 190, 196-209, 230-303, 319, 342 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/server_lib.py 200 151 24% 57-95, 146-150, 153-164, 173, 184, 194, 213, 235, 285-313, 318, 324, 327, 330-334, 348-361, 365, 374, 388-392, 407-411, 427-434, 457-464, 473-491, 527-530, 534-538, 542-546, 555-573 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_manager.py 149 120 19% 42-47, 144-155, 189-227, 288-319, 353-383, 411-442, 453-459, 473, 487, 502-512, 529-551, 557-558, 561-562 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/session_run_hook.py 41 13 68% 110, 127, 150, 169, 186, 211, 226-228, 240, 245, 255, 263 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/slot_creator.py 62 48 23% 55-101, 124-135, 161-173, 190-204 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/summary_io.py 12 1 92% 80 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/supervisor.py 339 239 29% 308-357, 360-369, 380-390, 405-416, 427-433, 444-455, 464-469, 478-484, 493-500, 509, 518, 530, 539, 548, 557, 561, 570, 579, 588, 597, 606, 615, 624, 628-636, 661-688, 720-745, 772-780, 801-808, 831-847, 859, 869, 879, 883, 898-902, 910-918, 928-931, 999-1023, 1038-1040, 1043-1051, 1066-1073, 1076-1077, 1081-1098, 1112-1114, 1117-1122 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/sync_replicas_optimizer.py 150 114 24% 181-205, 223, 248-358, 375-378, 392, 403, 417, 439-458, 462, 476-478, 481-497, 501-513 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/base.py 342 122 64% 71-76, 79-82, 86, 93-95, 98, 112, 126, 143-155, 161, 165, 170-173, 181-183, 187, 211, 233, 241-253, 266, 277-278, 291-307, 320, 323-324, 338-348, 352-354, 364-367, 369, 373, 376, 411, 414, 489-494, 521-526, 544-546, 550, 558, 562, 566, 570, 601, 624, 629, 633, 637-640, 726, 736-737, 754, 780-790, 822, 825, 829-839, 876, 883, 923-925, 933, 1004-1005, 1022-1023 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/data_structures.py 536 236 56% 28-30, 70, 72, 74, 92, 94, 97, 144, 167, 171, 184, 187, 193, 205, 214, 221, 228, 232, 236, 240, 248-252, 257-261, 266, 271, 314, 318, 321, 324, 328, 336, 349-350, 353, 356-366, 369, 372, 375, 381, 387, 390, 444-445, 454-455, 459-462, 465-468, 472, 482, 484-485, 494, 501, 510, 522-523, 532-545, 550, 568-574, 577, 580, 583, 586, 589, 592, 597, 600-601, 604-605, 608, 611-612, 626, 629, 646-648, 654, 657, 660, 666-669, 672-676, 679-683, 689-690, 693, 696, 699, 702, 717, 720, 732, 747, 751-754, 757-760, 770, 777, 785, 806, 808-809, 815, 823-824, 828-832, 845-859, 862-864, 867, 870, 875, 878-879, 882, 892-938, 943, 947-954, 957, 962, 967, 970, 973, 976, 983, 987, 991-998, 1001-1009, 1013, 1048-1055 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/graph_view.py 201 126 37% 78-86, 97-135, 177-179, 192, 211-312, 319-330, 335-356, 379-380, 385-402 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/layer_utils.py 110 13 88% 33-34, 190, 231, 242, 268, 293-296, 301-304 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/python_state.py 17 1 94% 87 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/tracking.py 131 52 60% 82, 92-94, 98, 102-113, 119-125, 140, 177-178, 185-186, 190-191, 221, 226, 231-234, 237-252, 274-276, 323-324, 330 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py 602 378 37% 69-72, 90-115, 129, 133, 136, 143-145, 147-149, 151, 229, 244, 248-249, 252-254, 272-277, 285, 291-294, 302-308, 324-348, 355-379, 392-418, 433, 465-476, 493, 511-512, 537-607, 616, 621, 626, 631, 636, 640, 651-664, 710-740, 763, 780, 788-806, 810-814, 831-845, 849-850, 864-867, 871, 876, 881, 893, 911-921, 941-945, 959, 963-982, 990, 998, 1002-1014, 1018-1024, 1029, 1036-1037, 1040-1045, 1094-1107, 1125-1140, 1164-1198, 1259, 1263, 1268-1281, 1286-1290, 1333-1335, 1343, 1468-1481, 1485-1490, 1520-1529, 1540-1541, 1568-1601, 1705-1712, 1808-1821, 1825-1830, 1857-1866, 1877-1878, 1902-1933, 2009-2016 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training.py 105 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_ops.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/training_util.py 95 65 32% 66-68, 88-103, 120-137, 158-162, 172-184, 201-209, 222-239, 243-253 /usr/local/lib/python3.8/dist-packages/tensorflow/python/training/warm_starting_util.py 150 123 18% 122-126, 152-156, 173-189, 240-311, 340-372, 396-411, 465-549 /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/user_ops/user_ops.py 10 1 90% 32 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/all_util.py 36 6 83% 78-83 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat.py 51 9 82% 60-61, 80, 86, 111, 116, 178-180 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/compat_internal.py 9 3 67% 34-36 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/decorator_utils.py 52 11 79% 26-32, 51, 71, 100, 108, 119, 145 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py 208 81 61% 95, 97, 102-110, 129-131, 188-239, 264, 314-317, 379, 381-382, 390, 439-442, 463-471, 485, 487, 489, 495, 497-500, 553, 565-568, 596-601, 635 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py 58 21 64% 69, 81, 100-104, 120-125, 128-131, 156, 170, 181-188 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/function_utils.py 59 38 36% 31-32, 36, 51-63, 78-86, 95-101, 106-119, 127-132 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/is_in_graph_mode.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lazy_loader.py 27 4 85% 50-52, 66-67 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/lock_util.py 43 3 93% 68, 92, 112 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/memory.py 11 4 64% 40-45 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/module_wrapper.py 132 60 55% 37, 44-48, 52, 68-78, 104-105, 108, 133-140, 144-152, 161-162, 170-174, 193-206, 219-227, 230-233, 236, 239 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/nest.py 303 121 60% 91-93, 100-101, 150, 157-162, 165, 167-171, 178-179, 181, 185, 220-221, 223-224, 231, 256, 332, 335, 379-382, 418-441, 485-486, 489, 496, 508-512, 596, 599, 605, 610, 653-657, 696, 777, 782, 789-804, 809-822, 827-828, 833, 837, 844, 1032-1037, 1189, 1249-1285, 1347-1351 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/object_identity.py 114 27 76% 44, 47-48, 51-52, 56, 61, 70, 114, 141, 148, 155, 159, 162-168, 179-181, 190, 199, 202, 205, 232 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/protobuf/compare.py 87 69 21% 94-118, 139-186, 190, 194-200, 216-255, 274 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/serialization.py 29 19 34% 43-76 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_contextlib.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_decorator.py 98 16 84% 172-173, 180-181, 188-193, 218, 222, 224, 257, 260, 272, 276 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_export.py 145 47 68% 114, 118, 132, 154, 174, 178, 180, 194-207, 220-228, 241-249, 274, 302, 307, 329-331, 343-344, 388-393 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_inspect.py 138 55 60% 34, 43, 79-90, 95, 118, 126, 131-147, 190-235, 282, 291-293, 298, 327, 342, 347, 362, 377, 382, 397, 402, 407 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_should_use.py 98 67 32% 45-61, 64-68, 72-86, 95, 100-101, 105-107, 113-117, 122-129, 148-161, 179-202, 235 /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/tf_stack.py 70 6 91% 37-38, 61, 76, 88, 99 /usr/local/lib/python3.8/dist-packages/tensorflow/tools/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/all_renames_v2.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/compatibility/renames_v2.py 5 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow/tools/docs/doc_controls.py 49 30 39% 258-261, 276-322 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/_api/v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/__init__.py 67 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/experimental/__init__.py 20 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/export/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/inputs/__init__.py 9 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/__init__.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/api/_v1/estimator/tpu/experimental/__init__.py 8 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/baseline.py 124 78 37% 72-79, 83-90, 95-112, 128-154, 187-197, 220-227, 264-283, 381-398, 414-427, 497-506, 516-525, 607-621, 636-650 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees.py 715 607 15% 61-81, 95-99, 111-115, 130-147, 168, 201-275, 280-287, 317-388, 393-414, 443-471, 476-489, 497, 505-570, 597-621, 625-639, 643-654, 674-680, 686, 690, 703-706, 709, 717-724, 730-731, 761-776, 824, 830-836, 841-889, 898, 906-911, 916-920, 926, 938-982, 986-1010, 1017-1052, 1056-1089, 1093, 1147-1407, 1414, 1417, 1440-1479, 1489-1506, 1513-1521, 1531-1551, 1556, 1565-1578, 1601-1615, 1641-1686, 1691-1700, 1739-1751, 1770-1781, 1840-1889, 1894-1903, 2038-2063, 2190-2214, 2315-2343, 2357-2360 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/boosted_trees_utils.py 42 27 36% 33-37, 45-57, 61, 66, 73-82, 87-94 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn.py 224 162 28% 46-48, 73-102, 125-153, 157-158, 174-230, 233-254, 260, 272, 287-344, 347-359, 363-364, 371-379, 431-456, 489-504, 552-579, 737-759, 787-807, 946-962, 986-1003, 1151-1172, 1199-1221 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/dnn_linear_combined.py 187 140 25% 43-44, 64-65, 69-71, 76-82, 141-226, 288-383, 554-585, 615-643, 657-682, 820-843, 864-888, 1048-1077, 1106-1136 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/head.py 497 410 18% 58, 78-89, 155, 166, 193, 230-240, 274, 299-354, 382-443, 448-472, 487-494, 510-520, 536-559, 563-567, 571-573, 579-584, 590-598, 615-617, 622-628, 632-637, 646-650, 658-666, 671-679, 738-748, 767-774, 778, 782, 787-808, 812-825, 829-852, 893-994, 1062-1077, 1096-1101, 1105, 1109, 1113-1195, 1199-1223, 1265-1373, 1435-1440, 1460-1467, 1471, 1475, 1479-1505, 1514-1532, 1571-1652, 1661-1664, 1668-1676, 1704-1715 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/kmeans.py 110 69 37% 46-48, 51-52, 55-62, 82-84, 87-99, 119-127, 137-145, 173-224, 404-415, 424-425, 436-438, 454, 470-475, 479 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear.py 283 217 23% 128-133, 138-174, 181-239, 243-244, 248-249, 262-270, 283-308, 326-375, 394-448, 470-539, 547-548, 555, 559, 583-619, 652-678, 719-748, 759-767, 920-945, 968-991, 1107-1119, 1158-1171, 1178-1186, 1325-1349, 1371-1396 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sdca_ops.py 291 245 16% 95-104, 114, 123, 132, 200-251, 254, 257, 261, 265, 270-271, 280-298, 305-307, 310-312, 316-318, 322-333, 337-348, 356-364, 368-371, 379-402, 420-433, 440-449, 464-629, 644-670, 679-699, 715-760, 775-781 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/linear_optimizer/python/utils/sharded_mutable_dense_hashtable.py 139 101 27% 73-99, 106-120, 124, 135-138, 158-165, 184-192, 204-210, 214, 223-229, 232-235, 262-280, 285, 289, 293, 296-298, 301-307, 310-311, 316-335, 339-352, 364-370 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/metric_keys.py 26 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/optimizers.py 46 27 41% 78-91, 95-97, 129-146 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/parsing_utils.py 44 25 43% 138-140, 254-257, 288-312, 321-324, 342-346 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/canned/prediction_keys.py 14 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/early_stopping.py 179 131 27% 84-104, 153, 210, 268, 326, 346-356, 365-388, 401-430, 446-450, 454-458, 471-479, 482-484, 487-488, 491-498, 505, 508, 511-512, 515-518, 525-529, 540-549, 552-566, 570-571, 577-592 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/estimator.py 729 611 16% 176-203, 209, 213, 217, 228-231, 246-248, 259-261, 270-271, 321-351, 365-376, 390, 445-462, 478-511, 525-543, 596-639, 646, 717-722, 800, 817-889, 926-1010, 1014-1017, 1020-1021, 1027-1039, 1043, 1048-1054, 1058-1072, 1086, 1097-1100, 1123-1137, 1154-1176, 1179-1182, 1197-1213, 1231-1241, 1250-1354, 1361-1385, 1390-1523, 1527-1563, 1567-1575, 1581-1631, 1636-1658, 1661-1664, 1735-1738, 1756, 1760-1763, 1769-1786, 1793-1805, 1822-1849, 1854-1861, 1871-1941, 1945-1947, 1953-1957, 1962-1968, 1985-1998, 2003-2016, 2021-2026, 2031-2040, 2052, 2065-2110, 2123-2134, 2141-2148, 2338-2341, 2366-2385 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export.py 128 88 31% 65-72, 77-95, 99-103, 139-157, 206-215, 238, 271-277, 302-313, 327-343, 348, 370-376, 404-440, 470-474 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_lib.py 27 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/export/export_output.py 16 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/exporter.py 151 106 30% 41, 62, 97-100, 104, 108-117, 136-145, 150-160, 255-271, 277, 281-307, 319-337, 350-365, 400, 406, 410-416, 457-462, 467, 471-477, 489-507 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/extenders.py 38 25 34% 82-94, 104-107, 113-123 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/gc.py 63 46 27% 87-95, 110-127, 140-147, 161-166, 179-184, 207-217 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/base_head.py 290 222 23% 110, 122, 133, 163, 178, 192, 224, 227, 281-295, 347, 406-469, 498-582, 587-624, 639-648, 652-654, 660-661, 666-667, 673-677, 695-705, 726-759, 763-765, 771-773, 779-784, 790, 798-817, 821-824, 844-855, 896-918, 925-934 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py 205 164 20% 151-193, 198, 203, 208, 229-232, 245-250, 254-259, 263-275, 284-300, 318-362, 366-392, 421-427, 430-433, 443-487, 540-597 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/head_utils.py 30 17 43% 55-66, 70-77, 92-94, 101-102 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_class_head.py 151 113 25% 143-168, 173, 178, 183, 204-207, 220-225, 229-242, 246-262, 271-287, 305-345, 349-359, 368-385, 436-492 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_head.py 182 149 18% 34-35, 40-45, 178-200, 205, 210, 215-220, 250-282, 286-306, 315-342, 346-353, 357-367, 376-395, 454-512, 528-548 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/multi_label_head.py 217 181 17% 161-243, 248, 253, 258, 277-280, 284-315, 322-339, 348-364, 381-399, 403-427, 436-480, 532-587 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/head/regression_head.py 124 85 31% 143-159, 164, 169, 174, 177-182, 186-203, 212-228, 242-254, 258-269, 279-297, 351-402, 483-484, 493, 568-570, 577 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/basic_session_run_hooks.py 31 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/hooks.py 92 67 27% 103-120, 124-141, 148-176, 179-201, 205-207, 211, 237-244, 247-249, 253, 256-266, 275-277 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/hooks/session_run_hook.py 13 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/numpy_io.py 70 56 20% 50-53, 70-86, 141-224 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/pandas_io.py 61 46 25% 32-36, 50-52, 91-158 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py 208 177 15% 34-38, 51-60, 78-96, 126-145, 158-169, 172-181, 197-212, 215-230, 243-255, 258-271, 285-297, 300-330, 376-504 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/usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tools/analytics.py 8 2 75% 28, 37 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/_tpu_estimator_embedding.py 227 180 21% 58, 62, 66-68, 73-94, 103-122, 137-174, 270-315, 337-358, 362-363, 368, 372-412, 415-418, 424-429, 434-490, 495-505, 510-519, 524-541, 553-557 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py 65 44 32% 56-58, 73-105, 115-117, 122-125, 136-154 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/iteration_count_estimator.py 64 46 28% 68, 72-81, 84, 87, 90, 107-111, 122-123, 132-150, 169-201 /usr/local/lib/python3.8/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_config.py 99 60 39% 141-189, 227-265, 272, 276, 280, 284, 288, 291-297, 304-309 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12079-12080, 12094-12110, 12121-12184, 12228-12477, 12482-12483, 12488-12495, 12500-12664, 12669-12674, 12679, 12686 /usr/local/lib/python3.8/dist-packages/tornado/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/tornado/concurrent.py 80 50 38% 53, 60-65, 68, 117-135, 154-171, 184-185, 204-207, 229-231, 238, 245, 261-264 /usr/local/lib/python3.8/dist-packages/tornado/escape.py 144 91 37% 54, 61, 75, 83, 88, 102-103, 108, 115, 137-144, 159-165, 173, 178, 183, 192-196, 204, 209, 214, 225-227, 245-256, 309-375, 379-390 /usr/local/lib/python3.8/dist-packages/tornado/gen.py 298 227 24% 97, 127-138, 142-153, 189-241, 254, 279-282, 343-359, 363-367, 375-380, 383-386, 392-397, 400, 403-406, 457, 481-523, 539-544, 586-622, 639-643, 660, 663, 706-714, 720-768, 771-791, 796-802, 808-811, 831-842 /usr/local/lib/python3.8/dist-packages/tornado/ioloop.py 272 176 35% 59-61, 68, 71, 170-179, 201, 215, 231, 236, 241, 264-279, 298, 309-313, 320, 324, 333-341, 368, 374, 380, 399, 408, 417, 425, 438-445, 458, 490-532, 546, 580-587, 602, 620, 629, 644, 654, 663, 680-696, 713-726, 733, 742-763, 767, 787-789, 803-809, 821-825, 835, 838, 871-877, 884-887, 891-894, 901, 904-911, 914-916, 919-946 /usr/local/lib/python3.8/dist-packages/tornado/locks.py 158 107 32% 27, 43-44, 48-51, 115-116, 119-122, 130-142, 146-154, 158, 202-203, 206, 213, 220-225, 232, 240-258, 271, 274, 282, 382-386, 389-395, 399-412, 422-440, 443, 451, 454, 462, 475-476, 480-482, 523, 526, 536, 545-548, 551, 559, 562, 570 /usr/local/lib/python3.8/dist-packages/tornado/log.py 111 87 22% 39-40, 44-45, 56-71, 75-78, 138-161, 164-207, 216-255, 267-336 /usr/local/lib/python3.8/dist-packages/tornado/platform/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py 156 116 26% 36, 45-75, 78-90, 95-104, 107-123, 126-135, 138-139, 142-151, 154, 162, 169, 172-184, 189-194, 202, 205, 224, 229, 253-261, 264-266, 269-275, 278-280, 292, 308, 314, 338-346 /usr/local/lib/python3.8/dist-packages/tornado/queues.py 147 96 35% 40, 62-70, 75, 78, 154-166, 171, 175, 178, 181-184, 199-207, 214-223, 246-252, 260-270, 284-288, 296, 299, 303, 306, 309, 314-316, 320-324, 327, 330, 333-342, 371, 374, 377, 404, 407, 410 /usr/local/lib/python3.8/dist-packages/tornado/util.py 177 116 34% 37-40, 51-63, 81-84, 87, 101, 114, 120, 128, 149-157, 161-165, 177-185, 198-203, 210-213, 228, 270-287, 299, 305, 308, 328-334, 340-350, 355-356, 361-363, 375-380, 383-395, 404-407, 421-430, 436, 448-452, 458, 462-465, 470-472 /usr/local/lib/python3.8/dist-packages/traitlets/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/_version.py 2 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/__init__.py 3 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/config/application.py 366 257 30% 54, 70, 74, 86-93, 113-117, 151-157, 177-181, 197-199, 209-229, 243-247, 273-282, 287-289, 297, 305-306, 310-332, 336-345, 348-359, 363-378, 385-406, 413, 419-421, 429-434, 438, 443-453, 469-498, 503-541, 550-589, 594, 599-609, 623-642, 646-650, 653-654, 662-664, 708-711 /usr/local/lib/python3.8/dist-packages/traitlets/config/configurable.py 190 142 25% 63-93, 102, 119-129, 134-168, 180-186, 196-200, 211-218, 227-250, 255, 260-286, 294-328, 342-343, 363-367, 373-379, 411-421 /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py 417 299 28% 53-55, 80, 83, 87, 91-93, 99-104, 108, 115-131, 138-147, 172-176, 180, 184-196, 206-216, 220-226, 232, 235, 238, 241-250, 260-266, 270-271, 277, 280-281, 285, 288-289, 292-297, 321-322, 339-344, 347, 356-357, 378-381, 385, 401-408, 411-412, 415-423, 426-427, 436-439, 452-458, 462-473, 477-489, 507-517, 521-527, 587-592, 596-597, 602-610, 636-686, 714-726, 738-748, 751-754, 757-758, 761, 766-768, 772-773, 782-807, 812-833, 846-857 /usr/local/lib/python3.8/dist-packages/traitlets/log.py 10 7 30% 18-27 /usr/local/lib/python3.8/dist-packages/traitlets/traitlets.py 1265 803 37% 52, 99-102, 110-118, 126-146, 153-156, 163-166, 173-178, 203-211, 217, 219, 221, 235-236, 245-252, 271-277, 281-285, 288-291, 294-297, 300-302, 325-332, 336-340, 343-346, 350-351, 406, 439-457, 473-475, 480-484, 499-514, 519-524, 527-543, 556, 559-574, 582-585, 588-594, 597-606, 609-612, 618-625, 632-637, 644-649, 660, 667, 681-686, 690-693, 697-706, 709-712, 729-732, 785, 788, 806-819, 848, 851, 894, 907, 914, 924, 933, 953-959, 965-977, 983-986, 992-1008, 1022-1028, 1031-1045, 1057-1065, 1076-1131, 1134, 1143-1176, 1179-1189, 1192-1198, 1228-1235, 1263-1265, 1284-1286, 1291-1297, 1319-1327, 1331-1334, 1338-1343, 1352, 1377-1387, 1395-1396, 1401, 1405, 1421-1437, 1441-1450, 1458-1459, 1476-1486, 1507, 1510-1524, 1561, 1566, 1574-1586, 1591, 1596, 1600-1601, 1604-1607, 1610-1614, 1655, 1660, 1664, 1666, 1674-1677, 1680-1688, 1691-1692, 1695-1696, 1699-1701, 1705, 1717-1718, 1746, 1752-1755, 1788-1790, 1793-1803, 1806-1809, 1812-1818, 1839-1853, 1869-1871, 1878-1882, 1886-1953, 1973-1977, 1984-1988, 1998-2002, 2009-2012, 2024-2026, 2033-2036, 2046-2054, 2061-2064, 2075-2082, 2087-2091, 2096-2101, 2111-2113, 2120-2123, 2136-2138, 2142-2145, 2155-2163, 2217, 2221, 2226, 2231-2233, 2236-2244, 2247-2257, 2265-2267, 2312-2314, 2317-2321, 2324-2326, 2416, 2421, 2429, 2433-2449, 2458-2461, 2501, 2504-2507, 2511-2515, 2517, 2524-2526, 2529-2533, 2536-2556, 2560, 2562-2563, 2567-2572, 2585-2591, 2602-2605, 2638-2645, 2649-2655, 2659-2663, 2666-2683, 2687-2690 /usr/local/lib/python3.8/dist-packages/traitlets/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/traitlets/utils/bunch.py 12 8 33% 12-15, 18, 22-24 /usr/local/lib/python3.8/dist-packages/traitlets/utils/getargspec.py 64 58 9% 22-86 /usr/local/lib/python3.8/dist-packages/traitlets/utils/importstring.py 17 13 24% 27-42 /usr/local/lib/python3.8/dist-packages/traitlets/utils/sentinel.py 8 1 88% 16 /usr/local/lib/python3.8/dist-packages/wcwidth/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_wide.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/table_zero.py 1 0 100% /usr/local/lib/python3.8/dist-packages/wcwidth/wcwidth.py 37 28 24% 116-129, 183-196, 212-220 /usr/local/lib/python3.8/dist-packages/wrapt/__init__.py 6 0 100% /usr/local/lib/python3.8/dist-packages/wrapt/decorators.py 186 91 51% 11-23, 40-41, 55-56, 60, 64, 68, 72, 76, 86, 91, 95, 99-102, 105-106, 112, 117-120, 123, 138, 142, 146, 149-150, 154, 158, 162-163, 165, 205, 208-212, 253-279, 292-294, 322, 343-390, 411, 444-445, 450-451, 454, 464-514 /usr/local/lib/python3.8/dist-packages/wrapt/importer.py 102 75 26% 12, 37-45, 52-98, 103-109, 112-119, 128-135, 145-148, 153, 156-159, 164, 172-221, 227-230 /usr/local/lib/python3.8/dist-packages/wrapt/wrappers.py 472 304 36% 11, 32, 36, 40, 44, 51, 60, 78-87, 91, 95, 99, 103, 107, 111, 114, 117, 121, 124, 130, 134, 138, 141, 144, 147, 150, 153, 156, 159, 162, 165, 168-190, 196-199, 202-216, 219, 222, 225, 228, 231, 234, 237, 240, 243, 246, 249, 252, 255, 258, 261, 264, 267, 270, 273, 276, 279, 282, 285, 288, 291, 294, 297, 300, 303-304, 307-308, 311-312, 315-316, 319-320, 323-324, 327-328, 331-332, 335-336, 339-340, 343-344, 347-348, 351-352, 355, 358, 361, 364, 367, 370, 373, 376, 379, 382, 385, 388, 391, 394, 397, 400, 403, 406, 409, 412, 415, 418, 421, 424, 427, 431, 437, 442-453, 456-461, 471-477, 505-533, 542-566, 578-624, 704-719, 727-728, 733-771, 774, 777-780, 791-794, 797-798, 801, 804, 807-811, 819-828, 831, 834-836, 839-858, 870-880, 899-928, 936-947 /usr/local/lib/python3.8/dist-packages/zmq/__init__.py 41 16 61% 23-24, 32-37, 54-60, 64-67 /usr/local/lib/python3.8/dist-packages/zmq/_future.py 323 280 13% 28-112, 129-144, 149, 152-161, 165-168, 176, 187, 196-199, 208-212, 216-241, 249-278, 282-289, 299, 308-315, 319-350, 354-406, 410-445, 448-483, 488-493, 503-509, 513-515, 519-521, 528, 532-533, 540-543 /usr/local/lib/python3.8/dist-packages/zmq/asyncio/__init__.py 48 20 58% 18-19, 28, 34-37, 41-43, 53, 60, 75-76, 83-87, 93 /usr/local/lib/python3.8/dist-packages/zmq/backend/__init__.py 26 15 42% 14-17, 22, 28-40 /usr/local/lib/python3.8/dist-packages/zmq/backend/cython/__init__.py 14 0 100% /usr/local/lib/python3.8/dist-packages/zmq/backend/select.py 15 7 53% 29-35 /usr/local/lib/python3.8/dist-packages/zmq/error.py 79 44 44% 37-50, 58, 61, 90-91, 101-102, 107-108, 120, 123-124, 132-144, 157-162, 165, 168, 183-184 /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:720: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_origin_portfolio_ids = [int(item) for item in options.list_origin_portfolio_ids.split(",")] if options.list_origin_portfolio_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:721: SyntaxWarning: "is not" with a literal. Did you mean "!="? list_photo_ids = [int(item) for item in options.list_photo_ids.split(",")] if options.list_photo_ids is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:722: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval = [int(item) for item in options.interval_rotation.split(",")] if options.interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:723: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval = [float(item) for item in options.interval_resize.split(",")] if options.interval_resize is not "" else None /home/admin/workarea/git/Velours/python/dev/generate_new_image.py:750: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi = [float(item) for item in options.mother_crop_portfolio_multi.split(",")] if options.mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/datou_lib.py:1505: SyntaxWarning: "is not" with a literal. Did you mean "!="? elif new_context_file is not "": /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1951: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1952: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:1958: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2142: SyntaxWarning: "is not" with a literal. Did you mean "!="? rotate_angle_interval_value = [int(item) for item in interval_rotation.split(",")] if interval_rotation is not "" else [] /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2143: SyntaxWarning: "is not" with a literal. Did you mean "!="? resize_interval_value = [float(item) for item in interval_resize.split(",")] if interval_resize is not "" else None /home/admin/workarea/git/Velours/python/mtr/datou/lib_step_exec/lib_step_pre_processing.py:2149: SyntaxWarning: "is not" with a literal. Did you mean "!="? mother_crop_portfolio_multi_value = [float(item) for item in mother_crop_portfolio_multi.split(",")] if mother_crop_portfolio_multi is not "" else None /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:84: SyntaxWarning: "is not" with a literal. Did you mean "!="? if (default_value.shape.ndims is not 0 /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py:85: SyntaxWarning: "is not" with a literal. Did you mean "!="? and default_value.shape.ndims is not 1): /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:285: SyntaxWarning: "is" with a literal. Did you mean "=="? minval_is_zero = minval is 0 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/random_ops.py:286: SyntaxWarning: "is" with a literal. Did you mean "=="? maxval_is_one = maxval is 1 # pylint: disable=literal-comparison /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:795: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/traitlets/config/loader.py:804: SyntaxWarning: "is" with a literal. Did you mean "=="? if len(key) is 1: /usr/local/lib/python3.8/dist-packages/zmq/eventloop/__init__.py 2 0 100% /usr/local/lib/python3.8/dist-packages/zmq/eventloop/ioloop.py 64 35 45% 23-26, 42-45, 49-52, 55-60, 64-67, 75, 89-91, 103-106, 114-117, 129-130, 135 /usr/local/lib/python3.8/dist-packages/zmq/eventloop/zmqstream.py 246 181 26% 44, 49, 53-54, 60-61, 114-138, 142, 146, 150, 154, 182-189, 200-203, 241-243, 255-258, 265, 271-279, 285-287, 295-299, 306-307, 311, 344-393, 397, 401-419, 423, 427, 430, 435-444, 449-469, 473-486, 491-505, 508-509, 513-522, 526-527, 531-532, 536-542, 546 /usr/local/lib/python3.8/dist-packages/zmq/sugar/__init__.py 15 0 100% /usr/local/lib/python3.8/dist-packages/zmq/sugar/attrsettr.py 29 21 28% 15-32, 36, 40-48, 52 /usr/local/lib/python3.8/dist-packages/zmq/sugar/constants.py 53 5 91% 34, 47, 104, 107-108 /usr/local/lib/python3.8/dist-packages/zmq/sugar/context.py 125 78 38% 27, 43-49, 53-54, 57, 60, 64, 77-79, 89-94, 119-132, 139-145, 152-154, 157-163, 180-192, 196, 210-222, 229, 236, 240-243, 247-253, 257-266 /usr/local/lib/python3.8/dist-packages/zmq/sugar/frame.py 29 12 59% 12-14, 51, 61-62, 66-67, 77-78, 82-83 /usr/local/lib/python3.8/dist-packages/zmq/sugar/poll.py 62 48 23% 22-23, 26, 44-57, 61, 71-75, 95-99, 126-152 /usr/local/lib/python3.8/dist-packages/zmq/sugar/socket.py 229 160 30% 32, 39-40, 59-63, 66-67, 75, 78, 86, 99-101, 104-106, 114-117, 124-132, 142-155, 166, 175-177, 186-188, 207-209, 229-231, 261-283, 290-300, 313-328, 390-400, 431-447, 475-481, 499, 519-520, 548-549, 566-568, 592-593, 610-611, 631-632, 646-651, 673-674, 697-704, 725-746, 754-755 /usr/local/lib/python3.8/dist-packages/zmq/sugar/stopwatch.py 15 11 27% 12-21, 25, 29-30 /usr/local/lib/python3.8/dist-packages/zmq/sugar/tracker.py 56 37 34% 54-63, 68-74, 96-118 /usr/local/lib/python3.8/dist-packages/zmq/sugar/version.py 20 7 65% 17-18, 26-29, 36, 41 /usr/local/lib/python3.8/dist-packages/zmq/utils/__init__.py 0 0 100% /usr/local/lib/python3.8/dist-packages/zmq/utils/constant_names.py 16 1 94% 549 /usr/local/lib/python3.8/dist-packages/zmq/utils/jsonapi.py 21 11 48% 26-27, 37-45, 53-56 /usr/local/lib/python3.8/dist-packages/zmq/utils/strtypes.py 23 13 43% 18-20, 24-29, 33-38 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ TOTAL 811029 561150 31% ret : 0 command : coverage3 html -i --omit=/usr/local/lib/python3.8/dist-packages/*,/home/admin/.local/lib/python3.8/site-packages/*,/usr/lib/python3/dist-packages/* -d htmlcov ret : 0 command : coverage3 report -i -m ret : 0 353.04user 132.43system 12:41.33elapsed 63%CPU (0avgtext+0avgdata 6510844maxresident)k 6952248inputs+995808outputs (16668major+13591428minor)pagefaults 0swaps